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Smart Health Prediction Using Data Mining
 
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Get the project at http://nevonprojects.com/smart-health-prediction-using-data-mining/ A smart system that suggests a persons disease and suggestions to cure based on his symptoms, also has online doctor to consult for further treatment and cure.
Views: 32433 Nevon Projects
Big Data and Challenges for Research and Research Funding
 
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Volker Markl, Chair, Database Systems and Information Management, University of Berlin (TU) Keynote at the Herrenhausen Conference "Big Data in a Transdisciplinary Perspective", 27.03.2015 VOLKER MARKL is a Full Professor and Chair of the Database Systems and Information Man¬agement (DIMA) group at the Technische Universität Berlin (TU Berlin) as well as an adjunct status-only professor at the University of Toronto. Earlier in his career, he lead a research group at FORWISS, the Bavarian Research Center for Knowledge-based Systems in Munich, and was a Research Staff member & Project Leader at the IBM Almaden Research Center in San Jose, California, USA. He has published numerous research papers on indexing, query optimization, lightweight information integration, and scalable data processing. He holdsseven patents, has transferred technology into several commercial products, and advises several companies and startups. He has been speaker and princi-pal investigator of the Stratosphere research project that resulted in the “ApacheFlink” big data analytics system. He was recently elected as one of Germany’s leading “digital minds” (Digitale Köpfe) by the German Informatics Society. Photo: Mirko Krenzel for Volkswagen Foundation ScienceUncut - Science Podcast by Volkswagen Foundation
Views: 774 VolkswagenStiftung
research paper topics in database
 
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Visit: https://goo.gl/TIo1T2?28784
Data Structures: Crash Course Computer Science #14
 
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Today we’re going to talk about on how we organize the data we use on our devices. You might remember last episode we walked through some sorting algorithms, but skipped over how the information actually got there in the first place! And it is this ability to store and access information in a structured and meaningful way that is crucial to programming. From strings, pointers, and nodes, to heaps, trees, and stacks get ready for an ARRAY of new terminology and concepts. Ps. Have you had the chance to play the Grace Hopper game we made in episode 12. Check it out here! http://thoughtcafe.ca/hopper/ Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 319524 CrashCourse
▶ Application of Data Mining - Real Life Use of Data Mining - Where We Can Use Data Mining ?
 
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Data Mining becomes a very hot topic in this moments because of its various uses. We can apply data mining to predict about an event that might happen. ✔Application of Data Mining - Real Life Use of Data Mining - Where We Can Use Data Mining? We're gonna learn some real-life scenario of Data Mining in this video. »See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on #Data_Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 #Learn_Data_Mining_In_A_Easy_Way #Data_Mining_Essential_Course #Data_Mining_Course_For_Beginner ট্র্যাডিশনাল পদ্ধতিতে যে সকল সমস্যার সহজে কোন সমাধান দেয়া যায় না #ডেটা_মাইনিং ব্যবহারে সহজেই একটি সিদ্ধান্তে পৌঁছানো সম্ভব। আর সে সিদ্ধান্ত কাজে লাগিয়ে ব্যবসায়িক অথবা যে কোন সম্পর্কিত সিদ্ধান্ত গ্রহন সম্ভব। Data Mining,big data,data analysis,data mining tutorial,book bd,Bangla tutorials,data mining software,Data Mining,What is data mining,bookbd,data analysis,data mining tutorial,data science,big data, business intelligence,data mining tools,bangla tutorial,data mining bangla tutorial,how to,how to mine data, knowledge discovery, Artificial Intelligence,Deep learning,machine learning,Python tutorials, Data Mining in the Retail Industry What does the future of business look like? How data will transform business? How data mining will transform business?
Views: 6047 BookBd
A survey Big Data social media using data mining techniques | | Final Year Projects 2016 - 2017
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 99 ClickMyProject
Final Year Projects | Data Mining with Big Data
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 14184 ClickMyProject
Data Mining For Thesis
 
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Views: 571 Suyeon Jung
An Internal Intrusion Detection and Protection System by Using Data Mining and Forensic Techniques
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 757 ClickMyProject
K mean clustering algorithm with solve example
 
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Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 233622 Last moment tuitions
mining text data projects
 
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Views: 66 PHD Projects
KDD2016 paper 150
 
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Title: Meta Structure: Computing Relevance in Large Heterogeneous Information Networks Authors: Zhipeng Huang*, The University of Hong Kong Yudian Zheng, The University of Hong Kong Reynold Cheng, The University of Hong Kong Yizhou Sun, Northeastern University Nikos Mamoulis, The University of Hong Kong Xiang Li, The University of Hong Kong Abstract: A heterogeneous information network (HIN) is a graph model in which objects and edges are annotated with types. Large and complex databases, such as YAGO and DBLP, can be modeled as HINs. A fundamental problem in HINs is the computation of closeness, or relevance, between two HIN objects. Relevance measures can be used in various applications, including entity resolution, recommendation, and information retrieval. However, few works have investigated the use of HIN information for relevance computation. In this paper, we propose the meta structure, which is a directed acyclic graph of object types with edge types connecting in between. The meta structure can describe complex relationship between two HIN objects (e.g., two papers in DBLP share the same authors and topics). We develop three relevance measures based on meta structure. Due to the computational complexity of these measures, we further design an algorithm with data structures proposed to support their evaluation. Our extensive experiments on YAGO and DBLP show that meta structure relevance is more effective than state-of-the-art approaches, and can be efficiently computed. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
Views: 238 KDD2016 video
What is DATA STREAM MINING? What does DATA STREAM MINING mean? DATA STREAM MINING meaning
 
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What is DATA STREAM MINING? What does V mean? DATA STREAM MINING meaning - DATA STREAM MINING definition - DATA STREAM MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities. In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion. Often, concepts from the field of incremental learning are applied to cope with structural changes, on-line learning and real-time demands. In many applications, especially operating within non-stationary environments, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e. the goal of the prediction, the class to be predicted or the target value to be predicted, may change over time. This problem is referred to as concept drift. Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data. Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.
Views: 397 The Audiopedia
To improve Blood Donation Process using Data Mining Techniques | Final Year Projects 2016
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 743 ClickMyProject
A Review on Mining Students’ Data for Performance Prediction  | Final Year Projects 2016 - 2017
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 419 ClickMyProject
Java in production for Data Mining Research projects (JavaDayKiev'15)
 
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Alexey Zinoviev presented this paper on the JavaDayKiev'15 conference Slides: http://www.slideshare.net/zaleslaw/javadaykiev15-java-in-production-for-data-mining-research-projects This paper covers next topics: Data Mining, Machine Learning, Hadoop, Spark, MLlib
Views: 309 Alexey Zinoviev
International Journal of Data Mining & Knowledge Management Process
 
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International Journal of Data Mining & Knowledge Management Process (IJDKP) ISSN : 2230 - 9608 [Online] ; 2231 - 007X [Print] http://airccse.org/journal/ijdkp/ijdkp.html Call for papers :- Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Topics of interest include, but are not limited to, the following: Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 144 aircc journal
IEEE DATAMINING TOPICS - FINAL YEAR IEEE COMPUTER SCIENCE PROJECTS
 
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TSYS Center for Research and Development (TCRD) is a premier center for academic and industrial research needs. We at TRCD provide complete support for final year Post graduate Student (M.E / M.Tech / M. Sc/ MCA/ M-phil) who are doing course in computer science and Information technology to do their final year project and journal work. For Latest IEEE DATA MINING Projects Contact: TSYS Center for Research and Development (TSYS Academic Projects) Ph.No: 9841103123 / 044-42607879, Visit us: http://www.tsys.co.in/ Email: [email protected] IEEE TRANSACTION ON KNOWLEDGE AND DATA ENGINEERING 2016 TOPICS 1. A Simple Message-Optimal Algorithm for Random Sampling from a Distributed Stream 2. Online Learning from Trapezoidal Data Streams 3. Quality-Aware Subgraph Matching Over Inconsistent Probabilistic Graph Databases 4. CavSimBase: A Database for Large Scale Comparison of Protein Binding Sites 5. Online Subgraph Skyline Analysis over Knowledge Graphs 6. K Nearest Neighbour Joins for Big Data on MapReduce: a Theoretical and Experimental Analysis 7. ATD: Anomalous Topic Discovery in High Dimensional Discrete Data 8. Multilabel Classification via Co-evolutionary Multilabel Hypernetwork 9. Learning to Find Topic Experts in Twitter via Different Relations 10. Analytic Queries over Geospatial Time-Series Data Using Distributed Hash Tables 11. RSkNN: kNN Search on Road Networks by Incorporating Social Influence 12. Unsupervised Visual Hashing with Semantic Assistant for Content-based Image Retrieval 13. A Scalable Data Chunk Similarity based Compression Approach for Efficient Big Sensing Data Processing on Cloud 14. Network Motif Discovery: A GPU Approach 15. Crowdsourced Data Management: A Survey 16. Resolving Multi-Party Privacy Conflicts in Social Media 17. Improving Construction of Conditional Probability Tables for Ranked Nodes in Bayesian Networks 18. Clearing Contamination in Large Networks 19. Private Over-threshold Aggregation Protocols over Distributed Databases 20. Challenges in Data Crowdsourcing 21. Efficient R-Tree Based Indexing Scheme for Server-Centric Cloud Storage System
Facebook Friend Recommendation using Graph Mining | AI Case Study
 
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Views: 1189 DigiiMento Education
Introduction to Datawarehouse in hindi
 
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Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 209458 Last moment tuitions
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
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In this video Apriori algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 139706 Well Academy
International Journal of Data Mining & Knowledge Management Process (IJDKP)
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 159 ijdkp jou
Data Analytics for Beginners | Introduction to Data Analytics | Data Analytics Tutorial
 
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Data Analytics for Beginners -Introduction to Data Analytics https://acadgild.com/big-data/data-analytics-training-certification?utm_campaign=enrol-data-analytics-beginners-THODdNXOjRw&utm_medium=VM&utm_source=youtube Hello and Welcome to data analytics tutorial conducted by ACADGILD. It’s an interactive online tutorial. Here are the topics covered in this training video: • Data Analysis and Interpretation • Why do I need an Analysis Plan? • Key components of a Data Analysis Plan • Analyzing and Interpreting Quantitative Data • Analyzing Survey Data • What is Business Analytics? • Application and Industry facts • Importance of Business analytics • Types of Analytics & examples • Data for Business Analytics • Understanding Data Types • Categorical Variables • Data Coding • Coding Systems • Coding, coding tip • Data Cleaning • Univariate Data Analysis • Statistics Describing a continuous variable distribution • Standard deviation • Distribution and percentiles • Analysis of categorical data • Observed Vs Expected Distribution • Identifying and solving business use cases • Recognizing, defining, structuring and analyzing the problem • Interpreting results and making the decision • Case Study Get started with Data Analytics with this tutorial. Happy Learning For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 193431 ACADGILD
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] ******************************************************************* Call for Papers ============== Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations ======================= Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications ======================== Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing ==================== Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission **************** Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 45 aircc journal
#Data: 7 hot topics for 2018 - 5. Data Science
 
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The year 2018 looks promising, more than ever driven by Data. What are specifically the trends and topics to track? Fifth topic: "Data Science ". Watch here!
Views: 389 Business & Decision
Data Mining with Big Data IEEE DOT NET 2014
 
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Frontline offers Final Year IEEE Projects. Get the abstract, project source code, documentation ,ppt and UML Diagrams. Online Demo and Training Sessions available. Frontline India visit us at frontl.in Call +91 7200 247 247 or mail us at [email protected] Online Training Sessions available
International Journal of Data Warehousing and Mining
 
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International Journal of Data Warehousing and Mining David Taniar (Monash University, Australia) Now Available Year Established: 2005 Publish Frequency: Quarterly ISSN: 1548-3924 EISSN: 1548-3932 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.20170101 ___________ Description: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving data. ___________ Topics Covered: Algorithms Applications issues Data mart Data mining Data mining methods Data models Data structures Data warehousing Data warehousing process Design Knowledge discovery process Mining databases Online analytical process Practical issues Tools and languages ___________ Indexed and Abstracted In: ABI/Inform ACM Digital Library Australian Business Deans Council (ABDC) Burrelle's Media Directory Cabell's Directories Compendex (Elsevier Engineering Index) CSA Illumina Current Contents®/Engineering, Computing, & Technology DBLP DEST Register of Refereed Journals Gale Directory of Publications & Broadcast Media GetCited Google Scholar INSPEC JournalTOCs Library & Information Science Abstracts (LISA) MediaFinder SCOPUS The Index of Information Systems Journals The Standard Periodical Directory Ulrich's Periodicals Directory Web of Science (All Journals) Web of Science Science Citation Index Expanded (SCIE)
Views: 142 IGI Global
Topic Model for Graph Mining | Final Year Projects 2016
 
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Views: 72 ClickMyProject
Data Mining Research Projects | Data Mining Research Thesis | Data Mining Research Code Projects
 
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Contact Best Matlab Simulation Projects Visit us: http://matlabsimulation.com/
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Final Year Projects 2015 | Automated web usage data mining and recommendation system
 
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Views: 521 ClickMyProject
A Review Paper on Dengue Disease Forcasting Using Data Mining Techniques
 
08:46
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577 Shop Now @ https://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
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International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:09
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum.Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects,surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining,Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining. Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks,Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing,OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper submission Authors are invited to submit papers for this journal through e-mail: [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 48 aircc journal
Big Data Analytics in Mobile Cellular Networks | Final Year Projects 2016 - 2017
 
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Views: 342 myproject bazaar
A Novel Data Mining Approach for Soil Classification | Final Year Projects 2016 - 2017
 
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Views: 47 ClickMyProject
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 99844 LearnEveryone
A Big Data Perspective (ACM SIGKDD 2016 Innovation Award)
 
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Author: Philip S. Yu, Department of Computer Science, College of Engineering, University of Illinois at Chicago More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 320 KDD2016 video
Data Mining Project Ideas for Students | Data Mining Thesis Ideas for Students - Semalt
 
02:06
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Views: 13 cicero menezes
Soil Classification Using Data Mining Techniques: A Comparative Study | Final Year Projects 2016
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 167 ClickMyProject
"The Marriage of BI and Big Data Business unIntelligence," Barry Devlin
 
01:02:47
As big data and business analytics become the norm, companies with existing data warehouse architectures are worrying how these new approaches relate to traditional BI and what must be done to implement new analytic systems. Unfortunately, much current advice focuses on what’s new rather than what to do to get from current systems to fully integrated big data and analytics and reap the clear benefits. What is needed is a new information architecture that combines the best of current data warehousing approaches and facilitates integration of what is new. This webinar is based on Barry Devlin's new book Business unIntelligence - Insight and Innovation Beyond Analytics and Big Data and will cover: -Business drivers and results of the emerging biz-tech ecosystem -Modern conceptual and logical architectures for information, process and people -Positioning of all forms of business analytics and big data Duration: 60 minutes (including audience Q&A) Presenter: Barry Devlin, Founder and Principal, 9sight Consulting. Dr. Barry Devlin is among the foremost authorities on business insight and one of the founders of data warehousing, having published the first architectural paper on the topic in 1988. With over 30 years of IT experience, including 20 years with IBM as a Distinguished Engineer, he is a widely respected analyst, consultant, lecturer and author of the seminal book, Data Warehouse—from Architecture to Implementation, and numerous white papers, blogs and more. His new book Business unIntelligence—Insight and Innovation Beyond Analytics and Big Data was published in 2013. Barry is founder and principal of 9sight Consulting. He specializes in the human, organizational and IT implications of deep business insight solutions in all technology environments. Barry is based in Cape Town, South Africa and operates worldwide. Moderator: Peter Aiken, Founding Director, Data Blueprint; Associate Professor of Information Systems, Virginia Commonwealth University; ACM SIGMIS. Peter Aiken is widely acclaimed as one of the top ten data management authorities worldwide. As a practicing data consultant, author and researcher, he has been actively performing in and studying data management for more than 30 years. Throughout his career, he has held leadership positions and consulted with more than 50 organizations in 20 countries across numerous industries, including defense, banking, healthcare, telecommunications and manufacturing. He is a highly sought-after keynote speaker and author of multiple publications, including his latest book, The Case for the Chief Data Officer: Recasting the C-Suite to Leverage Your Most Valuable Asset. In addition to being Data Blueprint’s Founding Director, Peter is also Associate Professor of Information Systems at Virginia Commonwealth University and past President of the International Data Management Association (DAMA).
International Journal of Data Mining & Knowledge Management Process (IJDKP)
 
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International Journal of Data Mining & Knowledge Management Process (IJDKP) ISSN : 2230 - 9608 [Online] ; 2231 - 007X [Print] http://airccse.org/journal/ijdkp/ijdkp.html Call for papers :- Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Topics of interest include, but are not limited to, the following: Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 90 Sivakumar Arumugam
Machine Learning with Small Data Sets in the Age of Deep Learning
 
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Dr. Lei Tang and Dr. Xin Xu will talk about how they apply machine learning with small data sets in sales management and forecast. The recent successes of machine learning and deep learning can be largely attributed to three factors: emergence of abundant data, development of innovative algorithms, and availability of machine learning tools and computing resources. Unfortunately, not all application spaces provide data sets large enough to be used in the usual or obvious ways. In this talk, Lei and Xin focus on one specific domain, enterprise sales, where data is often limited in volume, always noisy, and constantly evolving. They describe how machine learning, and in particular deep learning, can help, and how we address the data challenges described. They specifically discuss how to select model architectures appropriate for these limited data situations, for example, how deep our networks should be. By sifting through sales records and associated sales activities Lei and Xin enable identification of at-risk opportunities as well as project and estimated the time required to close each deal. This, in turn, contributes to the generation of a reliable business forecast for sales managers and executives. Lessons and findings learned through the process is shared. Speaker Bios: Dr. Lei Tang is the Chief Data Scientist at Clari Inc., a startup backed by Sequoia Capital and Bain Capital ventures, focusing on predictive analytics for sales execution and forecasting. Lei received his Ph.D. in computer science from Arizona State University in 2010, and B.S. from Fudan University, China. He is passionate about reshaping variety of businesses, driving business growth and decision through data science and machine learning. From 2012-2014, Lei was the lead data scientist at Demand Generation of @WalmartLabs, where he worked closely with marketing team to drive traffic to site, impacting hundreds of revenue each year. Before that, Lei had 2-year stint at advertising sciences in Yahoo! Labs, working on targeting, user profiling/segmentation by mining user behavioral, social and content information. Lei has co-authored one book on “community detection and mining in social media” (top-download in the corresponding data mining lecture series), held 4 patents, published over 30 papers at top-notch conferences and journals on data mining/machine learning, with over 4000 citations. Dr. Xin Xu is currently working as a data scientist in Clari. Before this, She received her Ph.D degree in Computer Engineering from North Carolina State University in 2015. She also did summer intern in Bell Labs and Akamai Technology in 2014 and 2015 respectively. Her current research interest mainly focuses on applying data mining, machine learning and advanced analytics to solve practical problems in sales domain.
PRIVACY PRESERVING OUTSOURCED ASSOCIATION RULE MINING ON VERTICALLY PARTITIONED DATABASES
 
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Association rule mining and frequent itemset mining are two popular and widely studied data analysis techniques for a range of applications. In this paper, we focus on privacy-preserving mining on vertically partitioned databases. In such a scenario, data owners wish to learn the association rules or frequent itemsets from a collective data set and disclose as little information about their (sensitive) raw data as possible to other data owners and third parties. To ensure data privacy, we design an efficient homomorphic encryption scheme and a secure comparison scheme. We then propose a cloud-aided frequent itemset mining solution, which is used to build an association rule mining solution. Our solutions are designed for outsourced databases that allow multiple data owners to efficiently share their data securely without compromising on data privacy. Our solutions leak less information about the raw data than most existing solutions. In comparison to the only known solution achieving a similar privacy level as our proposed solutions, the performance of our proposed solutions is three to five orders of magnitude higher. Based on our experiment findings using different parameters and data sets, we demonstrate that the run time in each of our solutions is only one order higher than that in the best non-privacy-preserving data mining algorithms. Since both data and computing work are outsourced to the cloud servers, the resource consumption at the data owner end is very low. To get the source code contact 9003628940
Views: 60 IEEE PROJECTS
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum.Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/ visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 28 aircc journal
Data Mining - Sociology of Science & Organizations | Lectures On-Demand
 
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Jason Owen Smith The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:11
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum.Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/ visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 19 aircc journal
Text Mining in Publishing
 
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TEXT MINING AND SCHOLARLY PUBLISHING: This short video by John Bond of Riverwinds Consulting discusses Text Mining and the Scholarly Publishing Industry. MORE VIDEOS on TEXT MINING and Scholarly Publishing can be found at: https://www.youtube.com/playlist?list=PLqkE49N6nq3jY125di1g8UDADCMvCY1zk FIND OUT more about John Bond and his publishing consulting practice at www.RiverwindsConsulting.com SEND IDEAS for John to discuss on Publishing Defined. Email him at [email protected] or see http://www.PublishingDefined.com CONNECT Twitter: https://twitter.com/JohnHBond LinkedIn: https://www.linkedin.com/in/johnbondnj Google+: https://plus.google.com/u/0/113338584717955505192 Goodreads: https://www.goodreads.com/user/show/51052703-john-bond YouTube: https://www.youtube.com/c/JohnBond BOOKS by John Bond: The Story of You: http://www.booksbyjohnbond.com/the-story-of-you/about-the-book/ You Can Write and Publish a Book: http://www.booksbyjohnbond.com/you-can-write-and-publish-a-book/about-the-book/ TRANSCRIPT: Hi there. I am John Bond from Riverwinds Consulting and this is Publishing Defined. Today I am going to discuss text mining as it relates to scholarly publishing. Text mining also goes by the phrase text data mining or text analytics. Text mining in scholarly publishing is the process of deriving high-quality information from peer reviewed articles and other content. It does this by processing large amounts of information and looking for patterns within the data, and then evaluating and interpreting the results. Text mining is most beneficial to researchers or other power users of technical content. It is very different from a keyword search such that you might perform with Google. A key word search likely produces thousands of web links with no uniformity in the results and certainly no ability to draw meaningful conclusions. An example: let’s say you are researching bladder cancer in men and you are looking for specific biomarkers for other disease states. You probably don’t have the time to review all the literature you might find through a search at PubMed. Text mining will review the available literature. It understands the parts of speech (nouns, verbs), recognizes abbreviations, takes term frequency into account, and other natural language processes. It will filter through all the content, extracts relevant facts, spot patterns, and provides the researcher with a more condensed set of results and statements than a literature search or a cursory review of abstracts ever could. It knows bladder cancer is a disease state. It knows, in this instance, to look for men as opposed to women. It understands what a biomarker is and how to apply this term to other disease states. It understands bladder cancer is a phrase and not being used as two separate terms. Text mining software involves high level programming and such concepts as word frequency distribution, pattern recognition, information extraction, and natural language processing as well as other programming concepts well beyond the scope of this video. The overall goal is to turn text into data for analysis and thereby help to draw conclusions. However, the results of text mining in and of themselves is not the end product, just part of the process. Individual text mining tools or enterprise level ones have become more common with researchers, librarians, and large for profit and not for profit organizations, and they will only grow. Aside from a text mining tool, an application is also necessary to check that the content being mined is licensed and to provide appropriate links to the content. Text mining is important to publishers or any group that holds large stores of full text articles or databases because this information as a whole has greater value than each individual part. Text mining can help extract that value. A key point for publishers is that the text mining tool and its user, such as a researcher, needs to have access to the content either by it being open access, through a subscription, or through a purchase. Subscription publishers see revenue when content is accessed or purchased. All publishers see article downloads and page views from text mining efforts. Either way, text mining as a tool in research, in medicine, in pharmaceutical R&D will only continue to grow in importance. Well that’s it. Please subscribe to my YouTube channel or click on the playlist to see more videos about text mining in scholarly publishing. And make comments below or email me with questions. Thank so much and take care.
Views: 255 John Bond
An Internal Intrusion Detection and Protection System by Using Data Mining and Forensic Techniques
 
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An Internal Intrusion Detection and Protection System by Using Data Mining and Forensic Techniques Website: - http://cloudstechnologies.in Like us on FB https://www.facebook.com/cloudtechnologiespro?ref=hl Follow us on https://twitter.com/cloudtechpro Cloud technologies is one of the best renowned software development company In Hyderabad India. We guide and train the students based on their qualification under the guidance of vast experienced real time developers.
Views: 455 Cloud Technologies
How to Import Data, Copy Data from Excel to R: .csv & .txt Formats (R Tutorial 1.5)
 
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Import/copy data from excel (or other spreadsheets) into #R using both comma-separated values and tab-delimited text file. Find more #RStats and #Statistics Tutorials here: https://goo.gl/4vDQzT ▶︎ You will learn to use "read.csv", "read.delim" and "read.table" commands along with "file.choose", "header", and "sep" arguments. This video is a tutorial for programming in #RStatisticalSoftware and #RStudio for beginners. You can access the dataset here: our website: http://www.statslectures.com/index.php/r-stats-videos-tutorials/getting-started-with-r/1-3-import-excel-data or here: Excel Data Used in This Video: http://bit.ly/1uyxR3O Excel Data Used in Subsequent Videos: https://bit.ly/LungCapDataxls Tab Delimited Text File Used in Subsequent Videos: https://bit.ly/LungCapData ◼︎Here is a quick overview of the topics addressed in this video; click on time stamps to jump to a specific topic: 0:00:17 the two main file types for saving a data file 0:00:36 how to save a file in excel as a csv file ("comma-separated value") 0:01:10 how to open a comma-separated (.csv) data file into excel 0:01:20 how to open a comma-separated (.csv) data file into a text editor 0:01:36 how to import comma-separated (.csv) data file into R using "read.csv" command 0:01:44 how to access the help menu for different commands in R 0:02:04 how to use "file.choose" argument on "read.csv" command to specify the file location in R 0:02:31 how to use the "header" argument on "read.csv" command to let R know that data has headers or variable names 0:03:22 how to import comma-separated (.csv) data file into R using "read.table" command 0:03:38 how to use "file.choose" argument on "read.table" command to specify the file location in R 0:03:41 how to use the "header" argument on "read.table" command to let R know the data has headers or variable names 0:03:46 how to use the "sep" argument on "read.table" command to let R know how the data values are separated 0:04:10 how to save a file in excel as tab-delimited text file 0:04:50 how to open a tab-delimited (.txt) data file into a text editor 0:05:07 how to open a tab-delimited (.txt) data file into excel 0:05:20 how to import tab-delimited (.txt) data file into R using "read.delim" command 0:05:44 how to use "file.choose" argument on "read.delim" command to specify the file path in R 0:05:49 how to use the "header" argument on "read.delim" command to let R know that the data has headers or variable 0:06:06 how to import tab-delimited (.txt) data file into R using "read.table" command 0:06:20 how to use "file.choose" argument on "read.table" command to specify the file location 0:06:23 how to use the "header" argument on "read.table" command to let R know that the data has headers or variable names 0:06:27 how to use the "sep" argument on "read.table" command to let R know how the data values are separated *****************************************************************************************To learn more: Subscribe: https://goo.gl/4vDQzT website: http://statslectures.com Facebook:https://goo.gl/qYQavS Twitter:https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at #UBC. Producer: Ladan Hamadani (B.Sc., BA., MPH)
Views: 519685 MarinStatsLectures