Home
Search results “Trend discovery data mining”
Current trends in Data Mining..
 
09:29
Topic described here are: Multimedia datamining Ubiquitous datamining Distributed datamining Spatial datamining Time series datamining Text mining Video mining Image mining Audio mining multimedia issues Submitted by: A. Vaishnavi II Msc cs A 175214141
Views: 622 vaishu raj
KDD ( knowledge data discovery )  in data mining in hindi
 
08:50
#kdd #datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 91328 Last moment tuitions
Lecture - 34 Data Mining and Knowledge Discovery
 
54:46
Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 135252 nptelhrd
[ باهر ] ما المقصود بالتنقيب عن البيانات ( data mining )
 
04:15
What id data mining ? Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Data mining tools allow enterprises to predict future trends. استخراج البيانات هي عملية فرز مجموعات البيانات الكبيرة لتحديد الأنماط وإقامة علاقات لحل المشاكل من خلال تحليل البيانات. تسمح أدوات استخراج البيانات للشركات بالتنبؤ بالاتجاهات المستقبلية Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.It is an essential process where intelligent methods are applied to extract data patterns. It is an interdisciplinary subfield of computer science. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. استخراج البيانات هي عملية الحوسبة لاكتشاف أنماط في مجموعات البيانات الكبيرة التي تنطوي على أساليب عند تقاطع التعلم الآلي، والإحصاءات، ونظم قواعد البيانات. وهي عملية أساسية حيث يتم تطبيق أساليب ذكية لاستخراج أنماط البيانات. وهو حقل فرعي متعدد التخصصات لعلوم الكمبيوتر. والهدف العام لعملية استخراج البيانات هو استخراج المعلومات من مجموعة بيانات وتحويلها إلى هيكل مفهوم لمزيد من الاستخدام. وبصرف النظر عن خطوة التحليل الخام، فإنه ينطوي على قواعد البيانات وإدارة البيانات، والمعالجة المسبقة للبيانات، واعتبارات نموذج والاستدلال، ومقاييس للاهتمام، واعتبارات التعقيد، مرحلة ما بعد المعالجة من الهياكل المكتشفة والتصور والتحديث عبر الإنترنت. استخراج البيانات هو خطوة تحليل "اكتشاف المعرفة في قواعد البيانات" العملية، أو ك.
Views: 284 Baher Elsaqqa
Data Mining Tutorial || Mr.Narayana Reddy || Introduction And Applications - Part-1
 
14:28
These Videos Will Make You To Perfect In Data Mining Introduction And Applications Of Data Mining ****************Subscribe For More Videos***************** Follow Me On Facebook : https://www.facebook.com/narayanaitechnologies
Introduction to data mining and architecture  in hindi
 
09:51
#datamining #datawarehouse #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 271259 Last moment tuitions
Data Warehouses Intro: Trends & landscape | The Daily Segment
 
05:52
Kicking off our series on the data warehouse landscape, we discuss some of the underlying trends in computing and infrastructure and the effects they’re having in the industry.
Views: 991 Segment
Google Analytics Data Mining with R (includes 3 Real Applications)
 
53:31
R is already a Swiss army knife for data analysis largely due its 6000 libraries but until now it lacked an interface to the Google Analytics API. The release of RGoogleAnalytics library solves this problem. What this means is that digital analysts can now fully use the analytical capabilities of R to fully explore their Google Analytics Data. In this webinar, Andy Granowitz, ‎Developer Advocate (Google Analytics) & Kushan Shah, Contributor & maintainer of RGoogleAnalytics Library will show you how to use R for Google Analytics data mining & generate some great insights. Useful Resources:http://bit.ly/r-googleanalytics-resources
Views: 30832 Tatvic Analytics
Data Mining (Introduction for Business Students)
 
04:21
This short revision video introduces the concept of data mining. Data mining is the process of analysing data from different perspectives and summarising it into useful information, including discovery of previously unknown interesting patterns, unusual records or dependencies. There are many potential business benefits from effective data mining, including: Identifying previously unseen relationships between business data sets Better predicting future trends & behaviours Extract commercial (e.g. performance insights) from big data sets Generating actionable strategies built on data insights (e.g. positioning and targeting for market segments) Data mining is a particularly powerful series of techniques to support marketing competitiveness. Examples include: Sales forecasting: analysing when customers bought to predict when they will buy again Database marketing: examining customer purchasing patterns and looking at the demographics and psychographics of customers to build predictive profiles Market segmentation: a classic use of data mining, using data to break down a market into meaningful segments like age, income, occupation or gender E-commerce basket analysis: using mined data to predict future customer behavior by past performance, including purchases and preferences
Views: 6468 tutor2u
Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
10:36
#datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 339489 Last moment tuitions
Data Mining trends and research frontiers
 
17:14
LC01_DM_GSLC13122018_2001609594_Muhammad Iqbal Sali Alparisi Anggota Kelompok 2001609594 ------- Muhammad Iqbal Sali A 2001610280 ------- Bagas Kuncoro 2001614663 ------- Achmad Rafii Syafran 2001616946 ------- Raditya Ayu Wirastari 2001618636 ------- Danny Hudi Pomo
Views: 46 Danny Hudi
Top 10 Trends In Data Science | Eduonix
 
11:30
Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured, similar to data mining. Data Science is spreading its roots gradually and becoming a hot topic of discussion everywhere. We have made a detailed video, which will tell you about all the recent trends which are going around in Data Science and if you're planning to choose your career in DS you will get a clearer idea about your path. We hope you like this video. The top trends mentioned in the video are: 1. Internet of Things (IoT) 2. Artificial Intelligence 3. Augmented Reality 4. Hyper Personalisation 5. Graph Analytics 6. Machine Intelligence 7. Agile Data Science 8. Behavioral Analytics 9. Journey Sciences 10. The Experience Economy Don't forget to check our new project on Data Science Foundational Program on Kickstarter. This program incorporates everything from beginner-level concepts to real-world implementation along with 4 courses, 2 e-books, Interview preparation guide, multiple labs, numerous practice tests and much more. Read more - https://kck.st/2CuIkay Thank you for watching! We’d love to know your thoughts in the comments section below. Also, don’t forget to hit the ‘like’ button and ‘subscribe’ to ‘Eduonix Learning Solutions’ for regular updates. https://goo.gl/BCmVLG Follow Eduonix on other social networks: ■ Facebook: https://goo.gl/ZqRVjS ■ Twitter: https://goo.gl/oRDaji ■ Google+: https://goo.gl/mfPaxx ■ Instagram: https://goo.gl/7f5DUC | @eduonix ■ Linkedin: https://goo.gl/9LLmmJ ■ Pinterest: https://goo.gl/PczPjp
Webinar: Top Trends for Moving Your Data to the Data Warehouse in the Cloud
 
39:34
Hosted by Attunity, Snowflake, and Wikibon on-demand webinar explains what you need to know about why, where, and how companies are diving into data warehousing on the cloud.
Views: 350 Snowflake Inc.
Data mining - definition
 
00:38
Data mining involves analysing databases for patterns and trends in large data sets. The overall goal of the data mining process is to extract knowledge from an existing data set and transform it into a human-understandable structure for further use. Created at http://www.b2bwhiteboard.com
Views: 9049 B2Bwhiteboard
Data Mining Trends and Research Frontiers - Kelompok Bo Cuan Gpp
 
12:52
Video Presentasi Data Mining Trends and Research Frontiers Kelompok Bo Cuan Gpp
Views: 701 Ria Liuswani
Data Mining for Individual Online Media Trends - On the Media
 
09:29
This clip compiled by BestOfTheLeft.com Subscribe to the full show this clip is from at www.onthemedia.org
Views: 89 Best Of The Left
Weka Data Mining Tutorial for First Time & Beginner Users
 
23:09
23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 474878 Brandon Weinberg
Time Series data Mining Using the Matrix Profile part 2
 
01:18:55
Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins Part 2 Authors: Abdullah Al Mueen, Department of Computer Science, University of New Mexico Eamonn Keogh, Department of Computer Science and Engineering, University of California, Riverside Abstract: The Matrix Profile (and the algorithms to compute it: STAMP, STAMPI, STOMP, SCRIMP and GPU-STOMP), has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, clustering etc. Link to tutorial: http://www.cs.ucr.edu/~eamonn/MatrixProfile.html More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 1287 KDD2017 video
International Journal of Data Mining & Knowledge Management Process (IJDKP)
 
00:36
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: 184 ijdkp jou
International Journal of Data Mining & Knowledge Management Process
 
00:11
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: 154 aircc journal
American Data's ECS Software - Data Mining, Tracking and Trends
 
02:46
www.american-data.com ECS is based on a foundation of daily point-of-care documentation. Daily assessments, evaluations, observations, progress notes, special assessments and departments are all linked together. Data is then populated to numerous reports and pulled into other areas of the medical record, saving time and reducing paperwork.
Views: 91 American Data
Turning to data for a trading edge · Dave Bergstrom, quant trader
 
55:33
EP 103: Escaping randomness, and turning to data for an edge w/ Dave Bergstrom On this episode, I’m joined by a quant trader who works at a high frequency trading firm—though you might be surprised to hear, he started out on the same path that many retail traders do—his name is; Dave Bergstrom. The thing that makes Dave unique from most traders who’ve been on this podcast previously, is how he uses data-mining techniques to develop trading strategies. Though data-mining, in trading, often has a negative connotation attached to it, Dave believes this stems from bad practices and poor evaluation of methods. In addition to the above and ways to reduce curve-fitting, we talk about escaping randomness, learning to write code, Dave’s three laws for strategy development, setting expectations and plenty more. -- Show notes: https://chatwithtraders.com/ep-103-dave-bergstrom/
Views: 16098 Chat With Traders
International Journal of Data Mining & Knowledge Management Process (IJDKP)
 
00:11
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: 94 Sivakumar Arumugam
Lecture - 35 Data Mining and Knowledge Discovery Part II
 
58:00
Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 43496 nptelhrd
What is Magellan Data Discovery?
 
00:52
Discover how this OpenText Magellan component makes it easier for anyone to access, blend, store and analyze data needed to identify trends and predict likely outcomes via a simply drag and drop experience.
Views: 74 OpenText
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:10
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. Important Dates **************** Submission Deadline : June 09, 2018 Notification : July 09, 2018 Final Manuscript Due : July 16, 2018 Publication Date : Determined by the Editor-in-Chief For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 4 aircc journal
Discovery 13: Transforming Your Business with High Performance Computing Panel
 
58:16
What if your company could use data mining to discover new information, identify new trends and correlations, and create new knowledge? What if you could safely simulate multi-million dollar physical assets and processes to accelerate your product development while preserving resources and reducing costs? What if we could analyze the human brain to develop preventative treatments for disorders and our physical environment to avert disasters? High Performance Computing (HPC) can equip your company with these kinds of competitive advantages. The Southern Ontario Smart Computing Innovation Platform (SOSCIP) enables Ontario businesses to leverage world-class HPC technology and collaborate with outstanding research talent to find new and innovative ways to drive revenue and growth. This panel features Ontario businesses and academics that are leveraging HPC to move to a new level of success. Co-Moderators: Ron Van Holst, Director, Research Development, High Performance Computing, Ontario Centres of Excellence Chris Pratt, BUE Strategic Initiatives Executive, IBM Canada Panellists: J. Wayne Gudbranson, President & CEO, Branham Group Inc. Dan Sinai, Associate Vice President, Research, Western University Abe Heifets, CEO, Chematria Dr. Jennifer Flexman, Director of Research Development and Commercialization, Sargent Laboratory, University of Toronto Ted Mao, Vice President, Research, Trojan UV For more information about Ontario Centres of Excellence (OCE), visit www.oce-ontario.org. To learn about OCE's Discovery conference, held annually in Toronto, Canada, visit www.ocediscovery.com.
APPLICATIONS OF DATA MINING
 
06:31
APPLICATIONS OF DATA MINING
Views: 2663 Samuel Hemandro
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:12
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: 17 Ijaia Journal
IBM Watson Discovery
 
07:28
This video is sponsored by IBM. Watson Discovery is an IBM Cloud service which allows you to unlock hidden value in data to find answers, monitor trends, and surface patterns using the world’s most advanced cloud-native insight engine. Watson Discovery ships with natural language processing built-in and can even be taught to understand terms that are specific to your domain. By automating the ingestion and processing of your data in a fully managed cloud service, it removes the complexity from your workflow and allows you to spend less time wrangling your data and more time building! Ready to get started with Watson Discovery? Sign up for your IBM Cloud Account: https://ibm.biz/ibm-cloud-signup Watson Discovery Documentation: https://ibm.biz/watson-discovery-docs Mining insights from data breaches (TUTORIAL): https://ibm.biz/discovery-insights-tutorial Create a stock information app (TUTORIAL): https://ibm.biz/stock-app-tutorial Code (+ Challenge) for this video: https://github.com/llSourcell/IBM_Watson_Discovery Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: https://www.theschool.ai/jobs/ #IBMWatson #IBM #SirajRaval Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 28487 Siraj Raval
Data on Purpose Panel: Data Mining for Social Impact
 
01:00:46
Experts discuss how data mining can help organizations effectively measure impact and optimize their work.
Views: 592 stanfordsocialinnov
Novel Data Mining Methods for Virtual Screening - PhD Defense
 
57:41
The Defense of PhD degree in Computer Science in King Abdullah University of Science and Technology (KAUST). Abstract: Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a confident conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by the big expansion in data and its dimensionality are inevitable. In this thesis, I address the main challenges that come when data mining techniques are used for virtual screening. In order to achieve an efficient virtual screening using data mining, I start by addressing the problem of feature selection and provide analysis of best ways to describe a chemical compound for an enhanced screening performance. High-throughput screening (HTS) assays data used for virtual screening are characterized by a great class imbalance. To handle this problem of class imbalance, I suggest using a novel algorithm called DRAMOTE to narrow down promising candidate chemicals aimed at interaction with specific molecular targets before they are experimentally evaluated. Existing works are mostly proposed for small-scale virtual screening based on making use of few thousands of interactions. Thus, I propose enabling large-scale (or big) virtual screening through learning millions of interaction while exploiting any relevant dependency for a better accuracy. A novel solution called DRABAL that incorporates structure learning of a Bayesian Network as a step to model dependency between the HTS assays, is showed to achieve significant improvements over existing state-of-the-art approaches.
Views: 489 Othman Soufan
IJDKP
 
00:41
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: 28 aircc journal
Statistical Aspects of Data Mining (Stats 202) Day 4
 
51:59
Google Tech Talks July 6, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 29381 GoogleTechTalks
Modernizing Real Estate with Data Science // Ian Wong, Opendoor (FirstMark's Data Driven)
 
24:26
Ian Wong, Co-Founder of Opendoor, spoke at Data Driven NYC on January 24th, 2017. He explained how Opendoor is utilizing data science to value and purchase homes around the country. Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark Capital.
Views: 7644 Data Driven NYC
Business Intelligence vs. Data Discovery
 
47:34
What exactly is Data Discovery? Is it just another BI tool or a passing trend? Shopper Technology Executive Director John Karolefski talks with Michael Scott of AFS Techologies about Data Discovery -- an appealing analytical tool which is replacing Business Intelligence as a way for manufacturers to better understand their business, improve their relationship with retaliers, and effectively gain a complete and consolidated business view.
IJDKP
 
00:13
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.
Views: 17 aircc journal
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:10
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: 23 aircc journal
2019 Amazon keyword research tool Keyword discovery tool | Keyword mining SellerSprite
 
05:24
(2019 English Version) Hello guys, In this video I will introduce you keyword mining tool which gives you relevant keywords along with Amazon search volume, search trends and other needed data. * Keyword mining helps you find the most powerful relevant keyword to improve listing, PPC, and therefor sales. * Keyword mining reflects the market demand and helps you analyze the market trend and product cycle. * Keyword mining helps you discover the profitable product. * Keyword mining helps you analyze the competitors. Start finding all keywords that help you make more money on Amazon : https://www.sellersprite.com/v2/keyword-miner People Also search: Amazon keyword research tool Amazon keyword tool Amazon seller tools Keyword inspector Amazon best keyword tool Amazon keyword tracker Amazon keywords tips Amazon fba Tutorial Amazon keyword ranking Free keyword tool,amazon keyword Amazon fba keyword rank Amazon fba long tail keywords How to rank for keywords on amazon How to sell on amazon ,that lifestyle ninja free amazon keyword research tool Amazon keyword ranking amazon keyword search volume keyword tool dominator amazon keyword tool
Views: 125 Seller Sprite
Visual Exploration of Smoking Cessation Data, presented by Polo Chau and Moushumi Sharmin
 
57:05
About the webinar Part 1: Discovery Dashboard Drs. Chau and Sharmin will present Discovery Dashboard [1], a visual analytics system for exploring large volumes of time series data from mobile medical field studies, in the web-browser and in real time. Discovery Dashboard offers interactive exploration tools and a data mining motif discovery algorithm to help researchers formulate hypotheses, discover trends and patterns, and ultimately gain a deeper understanding of their data. They will demonstrate their system visualizing data from a mobile sensor study conducted at the University of Minnesota that included 52 participants who were trying to quit smoking. Part 2: MyQuitPal The first step in designing effective smoking cessation systems is to objectively identify factors that contribute to lapse. To this end, we present MyQuitPal [2], a participant-centric cessation support system, which aims to assist individuals to better understand their smoking behavior. MyQuitPal combines an affective mobile application [2] and a web-based analytics tool [3] to support reflection. The design of MyQuitPal is informed by in-depth analysis of physiological data collected utilizing wearable sensors from a four day pre-quit, post-quit study (N=55). Visualizations presented in MyQuitPal are also grounded on theories of long term health-behavior change. About the presenters Polo Chau Ph.D., is an Assistant Professor at Georgia Tech’s School of Computational Science and Engineering, and an Associate Director of the MS Analytics program. He holds a Ph.D. in Machine Learning and a Masters in human-computer interaction (HCI). His Ph.D. thesis won Carnegie Mellon’s Computer Science Dissertation Award, Honorable Mention. His research group bridges data mining and HCI — innovates at their intersection — to synthesize scalable, interactive tools that help people understand and interact with big data. His group has created scalable deep learning visualization tools (deployed by Facebook), interactive graph querying system (SIGMOD'17 Best Demo, honrable mention), novel detection technologies for malware (patented with Symantec, protects 120M+ people), auction fraud (WSJ, CNN, MSN), comment spam (patented & deployed with Yahoo), fake reviews (SDM’14 Best Student Paper), insider trading (SEC), unauthorized mobile device access (Wired, Engadget); and fire risk prediction (KDD’16 Best Student Paper, runner up). He received faculty awards from Google, Yahoo, and LexisNexis. Dr. Chau also received the Raytheon Faculty Fellowship, Edenfield Faculty Fellowship, Outstanding Junior Faculty Award. He is the only two-time Symantec fellow. He leads the popular annual IDEA workshop that catalyzes cross-pollination across HCI and data mining. He served as general chair for ACM IUI 2015, and is a steering committee member of the conference. Moushumi Sharmin, Ph.D., is an Assistant Professor of Computer Science department at the Western Washington University. At Western, Dr. Sharmin co-directs the NEAT (Novel, Effective, Affective Technology) Research Lab, which focuses on designing participant-centric affective technology. Her research focuses on human-computer interaction, affective computing, and technology design. Currently she is investigating novel visualization techniques that support sense-making, pattern identification, and decision making of large scale data for behavioral health problems including autism spectrum disorder, and addiction, and harassment prevention. Students at the NEAT Lab have presented their work on addiction (GHC2017 - ACM SRC 2017 Runner-up (Undergraduate Category), CompSAC 2017), and autism (SIGCHI 2018). Dr. Sharmin is serving as the program committee chair for the Human Computing and Social Computing (HCSC) Symposium for IEEE CompSAC 2016, 2017 and 2018. She is a member of Google’s Women TechMakers and a fellow of the American Association of University Women. [1] mHealth Visual Discovery Dashboard. Dezhi Fang, Fred Hohman, Peter Polack, Hillol Sarker, Minsuk Kahng, Moushumi Sharmin, Mustafa al'Absi, Duen Horng (Polo) Chau. Demo, ACM International Joint Conference on Pervasive and Ubiquitous Computing (UBICOMP) Sept 11-15, 2017. Maui, USA. Paper: https://www.cc.gatech.edu/~dchau/papers/17-ubicomp-dashboard.pdf Video: https://youtu.be/vpvozWf1aCc [2] Opportunities and Challenges in Designing Participant-Centric Smoking Cessation System. Moushumi Sharmin, Theodore Weber, Hillol Sarker, Nazir Saleheen, Santosh Kumar, Shameem Ahmed, Mustafa al’ Absi. IEEE Computer Software and Applications Conference (COMPSAC), 2017, 835-844, Turin, Italy. Paper: https://www.academia.edu/35807876/Opportunities_and_Challenges_in_Designing_Participant-Centric_Smoking_Cessation_System Video: http://myweb.students.wwu.edu/webert3/
Views: 121 MD2K Center
#bbuzz 2015: Andrew Clegg - Signatures, patterns and trends: Timeseries data mining at Etsy
 
34:56
Find more information here: http://berlinbuzzwords.de/session/signatures-patterns-and-trends-timeseries-data-mining-etsy Etsy loves metrics. Everything that happens in our data centres gets recorded, graphed and stored. But with over a million metrics flowing in constantly, it’s hard for any team to keep on top of all that information. Graphing everything doesn’t scale, and traditional alerting methods based on thresholds become very prone to false positives. That’s why we started Kale, an open-source software suite for pattern mining and anomaly detection in operational data streams. These are big topics with decades of research, but many of the methods in the literature are ineffective on terabytes of noisy data with unusual statistical characteristics, and techniques that require extensive manual analysis are unsuitable when your ops teams have service levels to maintain. In this talk I’ll briefly cover the main challenges that traditional statistical methods face in this environment, and introduce some pragmatic alternatives that scale well and are easy to implement (and automate) on Elasticsearch and similar platforms. I’ll talk about the stumbling blocks we encountered with the first release of Kale, and the resulting architectural changes coming in version 2.0. And I’ll go into a little technical detail on the algorithms we use for fingerprinting and searching metrics, and detecting different kinds of unusual activity. These techniques have potential applications in clustering, outlier detection, similarity search and supervised learning, and they are not limited to the data centre but can be applied to any high-volume timeseries data. Kale version 1 is described here: https://codeascraft.com/2013/06/11/introducing-kale/ Version 2 has the same goals but a very different architecture and suite of tools. Come along if you'd like to learn more.