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Text-mining for rapid knowledge discovery
 
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Discussion as to how Elsevier Life Sciences Technologies help researchers gain access to insights and scientific data details without having to read dense, detailed articles in detail.
Text Mining and Knowledge Discovery with Knewco
 
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Knewco technology provides a platform that will empower Information users, researchers, and professionals in many fields. Internet users can search the Internet quicklyand discover knowledge efficiently. Knewco's Community Annotation enables domain experts and communities of interest to update information in real-time. Computer-based text mining and expert-based annotation delivers the optimal platform for knowledge discovery and understanding.
Views: 3249 jamesrchard
Lecture 1 — Overview Text Mining and Analytics - Part 1
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Text Mining with Big Data
 
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The video illustrates how text mining techniques allow the analysis of text written in natural language, in order to detect semantic relationships and enable text classification. Audio in Italian. English subtitles available. Illustrations developed by Monica Franceschini, Solution Architecture Manager, Big Data & Analytics Competency Center, Engineering Group.
Views: 468 ItalyMadeOpenSource
Knowledge Discovery From Data (KDD) Process (HINDI)
 
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Hello dosto mera naam hai shridhar mankar aur mein aap Sabka Swagat karta hu 5-minutes engineering channel pe. This channel is launched with a aim to enhance the quality of knowledge of engineering,here I am going to introduce you to every subject of computer engineering like artificial intelligence database management system software modeling and designing Software engineering and project planning data mining and warehouse data analytics Mobile communication Mobile computing Computer networks high performance computing parallel computing Operating system Software programming SPOS web technology internet of things design and analysis of algorithm
Views: 41464 5 Minutes Engineering
Final Year Projects | Effective Pattern Discovery for Text Mining | ClickMyProject
 
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Effective Pattern Discovery for Text Mining -Final Year Projects More Details: Visit http://clickmyproject.com/effective-pattern-discovery-for-text-mining-p-116.html 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:+91 967-778-1155 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us : [email protected]
Views: 6016 Clickmyproject
Text mining
 
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Pabitra Mitra - Research Scientist/Faculty at IIT Kharagpur speaks on text mining techniques
Lecture 2 — Overview Text Mining and Analytics - Part 2 | UIUC
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcleR
 
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Data Mining Using R (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com
Effective-Pattern-Discovery-for-Text-Mining 9860257109 www,gbsoftwares.com
 
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We provide softwares ,projects,ERP,E-Commerce,web services.web applications for all your needs. [email protected] gbproitsolutions.gmail.com contact-9860257109
Views: 69 aishwary gaikwad
Text Mining
 
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Views: 30 Sarah El-Kass
How Text Mining Tackles Key Challenges Facing Pharma, Biotech
 
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Dr. Jane Reed, head of life science strategy at Linguamatics, discusses how pharma and biotech companies use text analytics to reduce the time and cost of their clinical trials and get drugs to market faster. Founded in 2001 in Cambridge, UK, Linguamatics uses advanced Natural Language Processing (NLP) to read and understand both structured and unstructured data to quickly make connections between thousands of text-based sources for faster knowledge discovery and decision-making. See more at: https://businessvalueexchange.com/blog/2016/03/22/how-text-mining-tackles-key-challenges-facing-pharma-biotech-us/
Text Mining for Beginners
 
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This is a brief introduction to text mining for beginners. Find out how text mining works and the difference between text mining and key word search, from the leader in natural language based text mining solutions. Learn more about NLP text mining in 90 seconds: https://www.youtube.com/watch?v=GdZWqYGrXww Learn more about NLP text mining for clinical risk monitoring https://www.youtube.com/watch?v=SCDaE4VRzIM
Views: 78657 Linguamatics
Fabio Rinaldi and Lenz Furrer - Knowledge Discovery through Text Mining of the Biomedical Literature
 
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Presentation at "SwissText 2016" 08.06.2016 in Winterthur. http://www.swisstext.org Abstract: The goal of biomedical text mining is to automatically analyze the scientific literature in order to extract entities such as drugs, diseases, genes, and their relation-ships. Biomedical text mining is of great relevance for the pharmaceutical industry. On average, it costs about 1 billion dollars to develop a completely new medicinal drug, and it involves the work of hundreds of researchers. Text mining can help better target such experiments. The OntoGene group has developed a platform for advanced text mining applications, which sources its lexical resources from life sciences databases, thus allowing a deeper connection between the unstructured information contained in the literature and the structured information contained in databases. The quality of the system has been tested several times through participation in some of the community-organized evaluation campaigns, where it often obtained top-ranked results.
Views: 67 Swiss Text
What is Datamining | KDD process ?
 
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In this video I discussed about What is Data Mining and the process of Knowledge Discovery in Databases(KDD). Data mining is the process of discovering interesting patterns and knowledge from Huge amounts of data. The steps of KDD Process are : Data cleaning Data integration Data selection Data transformation Data mining Pattern evaluation Knowledge presentation
Views: 2605 DataMining Tutorials
Lecture - 34 Data Mining and Knowledge Discovery
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 135086 nptelhrd
Mining Unstructured Healthcare Data
 
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Deep Dhillon, former Chief Data Scientist at Alliance Health Networks (now at http://www.xyonix.com), presents a talk titled "Mining Unstructured Healthcare Data" to computational linguistics students at the University of Washington on May 8, 2013. Every day doctors, researchers and health care professionals publish their latest medical findings continuously adding to the world's formalized medical knowledge represented by a corpus of millions of peer reviewed research studies. Meanwhile, millions of patients suffering from various conditions, communicate with one another in online discussion forums across the web; they seek both social comfort and knowledge. Medify analyzes the unstructured text of these health care professionals and patients by performing a deep NLP based statistical and lexical rule based relation extraction ultimately culminating in a large, searchable index powering a rapidly growing site trafficked by doctors, health care professionals, and advanced patients. We discuss the system at a high level, demonstrate key functionality, and explore what it means to develop a system like this in the confines of a start up. In addition, we dive into details like ground truth gathering, efficacy assessment, model approaches, feature engineering, anaphora resolution and more. Need a custom machine learning solution like this one? Visit http://www.xyonix.com.
Views: 3821 zang0
Data mining
 
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Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. 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. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1714 Audiopedia
Text Mining Problems
 
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I would like to thank Lauren Briggs (Durban, South Africa) and Sean Pethybridge (Surf City, New Jersey) for giving voices to Laura, Saundra and Markus.
Views: 263 Fabio Stella
Data Mining & Business Intelligence | Tutorial #1 | The KDD Process
 
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Order my books at 👉 http://www.tek97.com/ #RanjiRaj #DataMining #KDD Understand the process of extracting knowledge from the facts listed under the KDD. There are 7 different steps to follow it. Watch it now! Comprender el proceso de extraer conocimiento de los hechos enumerados bajo el KDD. Hay 7 pasos diferentes para seguirlo. ¡Míralo ahora! فهم عملية استخراج المعرفة من الحقائق المدرجة تحت KDD. هناك 7 خطوات مختلفة لمتابعة ذلك. مشاهدته الآن! Verstehen Sie den Prozess der Extraktion von Wissen aus den unter der KDD aufgeführten Fakten. Es gibt 7 verschiedene Schritte, um es zu befolgen. Jetzt ansehen! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 11860 Ranji Raj
text mining, web mining and sentiment analysis
 
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text mining, web mining
Views: 1631 Kakoli Bandyopadhyay
Intro into Text Mining and Analytics - Chapter 1
 
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Text Mining and Analytics Intro into Text Mining and Analytics - Chapter 1 This video tutorials cover major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications. analytics | analytics tools | analytics software | data analysis programs | data mining tools | data mining | text analytics | strucutred data | unstructured data |text mining | what is text mining | text mining techniques More Articles, Scripts and How-To Papers on http://www.aodba.com
Views: 388 AO DBA
Effective Pattern Discovery for Text Mining 2012 IEEE JAVA
 
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Effective Pattern Discovery for Text Mining 2012 IEEE JAVA TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.
Views: 3799 jpinfotechprojects
Time Series data Mining Using the Matrix Profile part 1
 
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Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins Part 1 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: 2917 KDD2017 video
Text Mining of Presidential Campaign Speeches in R - Romney vs. Obama
 
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A conceptual presentation on how to build a machine learning system in R that uses text mining to predict the author of an unmarked presidential campaign speech. Commercial applications to brand & campaign management, SEO, electronic medical records (EMRs / EHRs), identity verification, fraud detection, and more. Code presentation to follow.
Views: 10112 Timothy DAuria
Webinar Text Mining: A new way to discover knowledge
 
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New computer software now enables us to screen over 20 million documents in a very short period of time, retrieving information from a large number of texts, including books, patents and scientific literature, as well as extracting relevant information and making combinations that are not easily thought of by scientists. The result: refreshing and unexpected links, and new knowledge discovery, providing new insights and routes for innovations in food. In this free webinar we will present state-of-the-art text mining software and showcase its applications in developing new food concepts. Topics covered during this 1 hr webinar are: - The importance of food dictionaries - An overview of the various text mining approaches - An overview of application possibilities
Oracle Fast Data Mining: Real Time Knowledge Discovery for Predictive Decision Making
 
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Fast Data as a different approach to Big Data for managing large quantities of "in-flight" data that help organizations get a jump on those business-critical decisions. Difference between Big Data and Fast Data is comparable to the amount of time you wait downloading a movie from an online store and playing the dvd instantly. Data Mining as a process to extract info from a data set and transform it into an understandable structure in order to deliver predictive, advanced analytics to enterprises and operational environments. The combination of Fast Data and Data Mining are changing the "Rules"
Views: 896 Nino Guarnacci
How NLP text mining works: find knowledge hidden in unstructured data
 
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Connect with us: http://www.linguamatics.com/contact What use is big data if you can't find what you're looking for? Follow: @Linguamatics https://twitter.com/Linguamatics https://www.linkedin.com/company/linguamatics https://www.facebook.com/Linguamatics https://plus.google.com/+Linguamatics https://www.youtube.com/user/Linguamatics/videos In knowledge driven industries such as the life sciences and healthcare, finding the right information quickly from huge volumes of text is crucial in supporting the best business decisions. However, around 80% of available information exists as unstructured text, and conventional keyword searches only retrieve documents, which still have to be read. This is very time consuming, unreliable, and, when important decisions rest on it, costly. Linguamatics’ text mining solution, I2E, uses Natural Language Processing to identify and extract relevant knowledge at least 10 times faster than conventional search, often uncovering insights that would otherwise remain unknown. I2E analyses the meaning of the text using powerful linguistic algorithms, enabling you to ask open questions, find the relevant facts and identify valuable connections. Going beyond simple keywords, I2E can recognise concepts and the different ways the same thing can be expressed, increasing the recall of relevant information. I2E then presents high quality results as structured, actionable knowledge, enabling fast review and analysis, and providing dramatically improved speed to insight. Our market leading software is supported by highly qualified domain experts who work with our customers to ensure successful project outcomes. Text mining for beginners: https://www.youtube.com/watch?v=40QIW9Sr6Io
Views: 17022 Linguamatics
Lecture 12: TextMining
 
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Techniques to extract information from textual data. Course homepage: http://www.knoesis.org/cs4800-6800-spring2016
Views: 1281 Knoesis Center
Text Mining lecture 4
 
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Text Mining Lecture 4 Topic: Making Words work: using financial text as a predator of financial events 1:12 Introduction 1:57 Background and literature 2:49 Vector space model 11:08 Methodology 24:00 Data 26:51 Testing Methodology 28:10 Results 31:27 Substitue- Complement test 32:32 Conclusion Topic: Textual Analyses in Accounting and Finance: A Survey 35:38 History of textual analyses 37:20 Background for business Textual Analyses 38:06 Related Literature 39:37 Challenges of textual analyses 41:54 Examples of studies using Readability 44:45 Defining and Measuring Readability 1:02:05 Bag of Words Methods 1:03:17 Word Lists 1:06:49 Zip’s Law 1:07:31 Term Weighting 1:07:58 Naive Bayes Methods 1:09:02 Thematic Structure in Documents 1:09:48 Implementation 1:11:28 Areas for future Research in Textual Analysis 1:12:40 Conclusion 1:13:44 Python Coding Please subscribe to our channel to get the latest updates on the RU Digital Library. To receive additional updates regarding our library please subscribe to our mailing list using the following link: http://rbx.business.rutgers.edu/subsc…
Seminar for Data Mining and Text Analytics     Omer Balola
 
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pp for (Syntactic Annotation Guidelines for the Quranic Arabic Dependency Treebank) paper By student: Omer Balola Ali, Supervised by: prof. Eric Atwell
Views: 112 omer ali
mod01lec01
 
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Views: 41777 Data Mining - IITKGP
Data Analytics: Week 3 : Data Preprocessing
 
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This is your week 3 lecture. Enjoy!
Views: 19109 Paul Kennedy
Time Series data Mining Using the Matrix Profile part 2
 
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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: 1204 KDD2017 video
Text/Data Mining, Libraries, and Online Publishers
 
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As more researchers embrace text- and data-mining methodologies, publishers must provide flexible and workable terms and utilities as they surmount legal and technological barriers to the new practices. This July 2013 webinar featured updates on the latest industry developments, with speakers from the journal publishing world, including: Eefke Smit, Director of Standards and Technology, STM, "Content Mining:A Short Introduction to Practices and Policies"; Carol Anne Meyer, Business Development and Marketing, CrossRef, "Prospect by CrossRef"; and Mark Seeley, Senior Vice President and General Counsel, Elsevier, "Enabling TDM: Contract Forms" This webinar was presented in cooperation with STM (International Association of Scientific, Technical & Medical Publishers) and ALPSP (Association of Learned and Professional Society Publishers).
Views: 1165 CRLdotEDU
Topic Detection Using Text Mining Project
 
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Get this project at http://nevonprojects.com/topic-detection-using-keyword-clustering/ System allows for automated topic detection using keyword clustering and analysis
Views: 3890 Nevon Projects
Design Mining the Web
 
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The Web has transformed the nature of creative work. For the first time, millions of people have a direct outlet for sharing their creations with the world. As a result, the Web has become the largest repository of design knowledge in human history, and the ensuing democratization of design has created a critical feedback loop, engendering a new culture of reuse and remixing. The means and methods designers use to employ to draw on prior work, however, remain mostly informal and ad hoc. How can content producers find relevant examples amongst hundreds of millions of possibilities and leverage existing design practice to inform and improve their creations? In this episode, P.h.D. candidate at Standford University, Ranjitha Kumar, explores data-driven techniques for working with examples at scale during the design process, automating search and curation, enabling rapid retargeting, and learning generative probabilistic models to support new design interactions. Knowledge discovery and data mining have revolutionized informatics; in this talk, Kumar discusses what we can learn from mining design.
Views: 1919 UW Video
Text Mining in JMP with R
 
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Some estimates suggest that unstructured text accounts for roughly 80 percent of the information stored by most organizations. This presentation by Andrew T. Karl, Senior Management Consultant at Adsurgo LLC, and Heath Rushing, Principal Consultant and Co-Founder of Adsurgo LLC, provides an overview of methods easily implemented with the R interface to JMP to find previously unknown relationships from a collection of unstructured data. By utilizing R packages for text mining and sparse matrix algebra, JMP may be equipped to extract information from text without requiring end-user knowledge of R. The text -- which may be from emails, survey comments, social media, incident reports, insurance claim reports, etc. -- may be used for several purposes. Vectors from a singular value decomposition of the document term matrix produced in R may be added to the original data table in JMP and included in predictive models (e.g., via the Fit Model or Neural platforms) or clustering algorithms (via the Cluster platform). Another goal may be to explore the underlying themes of the text though word counts or latent semantic indexing. We will demonstrate a JSL/R script that provides such functionality. This presentation was recorded at Discovery Summit 2013 in San Antonio, Texas.
Views: 5840 JMPSoftwareFromSAS
HSC English Advanced - Discovery Text Analysis
 
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In this HSC English Advanced video on Discovery text analysis, we show you exactly how to analyse your Discovery texts. Get keen! To watch more videos, head to our website at https://getatomi.com Subscribe to our channel for more FREE videos: http://youtube.com/user/HscHubVid Like us on Facebook for handy study tips and blog articles on how to smash your HSC: https://www.facebook.com/Hschub Follow Atomi on Instagram: https://www.instagram.com/get.atomi Follow Atomi news on Twitter: https://twitter.com/atomihq
Views: 9615 Atomi
Extracting Knowledge from Informal Text
 
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The internet has revolutionized the way we communicate, leading to a constant flood of informal text available in electronic format, including: email, Twitter, SMS and also informal text produced in professional environments such as the clinical text found in electronic medical records. This presents a big opportunity for Natural Language Processing (NLP) and Information Extraction (IE) technology to enable new large scale data-analysis applications by extracting machine-processable information from unstructured text at scale. In this talk I will discuss several challenges and opportunities which arise when applying NLP and IE to informal text, focusing specifically on Twitter, which has recently rose to prominence, challenging the mainstream news media as the dominant source of real-time information on current events. I will describe several NLP tools we have adapted to handle Twitter�s noisy style, and present a system which leverages these to automatically extract a calendar of popular events occurring in the near future (http://statuscalendar.cs.washington.edu). I will further discuss fundamental challenges which arise when extracting meaning from such massive open-domain text corpora. Several probabilistic latent variable models will be presented, which are applied to infer the semantics of large numbers of words and phrases and also enable a principled and modular approach to extracting knowledge from large open-domain text corpora.
Views: 4635 Microsoft Research
Text Analysis and Natural Language Processing Simplified | NLP Training | Edureka
 
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(NLP Certification Training: https://www.edureka.co/python-natural-language-processing-course) This video on "Text Analysis and Natural Language Processing" will provide you with in-depth knowledge of NLP, the different components of NLP and it's various applications in the industry along with a Python-based demo for each component. #nlp #deeplearning #textanalysis Subscribe to our Edureka YouTube channel to get video updates: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------- Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka ------------------------------------------------------------------------------------------------ For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 4144 edureka!
Filtering and Consolidation - Chapter 5
 
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Text Mining and Analytics Filtering and Consolidation - Chapter 5 This video tutorials cover major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications. analytics | analytics tools | analytics software | data analysis programs | data mining tools | data mining | text analytics | strucutred data | unstructured data |text mining | what is text mining | text mining techniques | AQL | Annotation Query Language More Articles, Scripts and How-To Papers on http://www.aodba.com
Views: 22 AO DBA
Large-scale Text Mining for Biological Data
 
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http://togotv.dbcls.jp/20110307.html#p01  In this video, Goran Nenadic who is a Senior Lecturer (Associate Professor) in the School of Computer Science, University of Manchester and a group leader in the Manchester Interdisciplinary BioCenter talks about text mining from biomedical literature. The talk has been at Workshop on Parallel and Distributed Processing of Large Genome Data organized by GCOE Program: Deciphering Genome Sphere from Genome Big Bang.
Views: 1543 togotv