PPT-CS583 – Data Mining and Text Mining

httpwwwcsuicedu liub CS583 Bing Liu UIC 2 General Information Instructor Bing Liu Email liubcsuicedu Tel 312 355 1318 Office SEO 931 Lecture times 930am1045am. Chapter 1. Kirk Scott. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Chapter 1. Kirk Scott. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). 1. Overview . This presentation is for chapter 16 which discuss :. Chapter . 16: Text Mining for Translational . Bioinformatics. 1- terminologies.. 2- definitions.. 2-uses cases and applications.. 3-evaluation techniques and evaluation metrics.. Opportunities and Barriers. John . McNaught. Deputy Director. National Centre for Text Mining. John.McNaught@manchester.ac.uk. Topics. What is text mining? (briefly). What can it offer? (selectively). AD103 - Friday, 3pm-4pm. Ben . Langhinrichs. President of Genii Software. Introduction. Ben Langhinrichs, Genii Software. When I am not developing software, I write children’s books and draw pictures.. Presentation by:. ABHISHEK KAMAT. ABHISHEK MADHUSUDHAN. SUYAMEENDRA WADKI. 1. Introduction. Mining the data to find interesting patterns, useful insights, customer data and their relationship - data mining . Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). in Robotics Engineering. Blink . Sakulkueakulsuk. D. . Wilking. , and T. . Rofer. , . Realtime. Object Recognition . Using Decision . Tree . Learning, 2005. . http. ://. www.informatik.uni-bremen.de/kogrob/papers/rc05-objectrecognition.pd. “The . word cloud . technique . first originated online in the 1990s as tag clouds (famously described as “the mullets of the . Internet”), . which were used to display the popularity of keywords in bookmarks. Cases . and Capabilities. Dec 8, 2016. Kayvis Damptey. Jie Zhang. What is Text Mining?. Text Mining uses documents to identify insightful patterns within the text. Thus allowing managers to summarize/organize huge collections of documents and automate detection based on useful linguistic patterns.. What Is . T. ext . M. ining?. Also known as . Text Data Mining. Process of . examining large collections of . unstructured. textual . resources in order to generate new information, typically using specialized computer software. interesting . and . useful. information from Web . content. and . usage . data. What is Web Mining?. Web mining is . a data . mining . technique . to extract knowledge from . web data. . . Web data includes : . Bamshad Mobasher. DePaul University. 2. From Data to Wisdom. Data. The raw material of information. Information. Data organized and presented by someone. Knowledge. Information read, heard or seen and understood and integrated.