PDF-DIAdem: Data Mining, Analysis, and Report Generation

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June 2010 373082H01 Worldwide Technical Support and Product InformationnicomNational Instruments Corporate Headquarters11500 North Mopac ExpresswayAustin Texas 787593504USATel

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DIAdem: Data Mining, Analysis, and Report Generation: Transcript

June 2010 373082H01 Worldwide Technical Support and Product InformationnicomNational Instruments Corporate Headquarters11500 North Mopac ExpresswayAustin Texas 787593504USATel 512 683 0100Worldwi. Mining . Methods Course. Dr. Russell Anderson. Dr. Musa Jafar. West Texas A&M University. What is Data Mining?. The process of discovering useful information in large data repositories. . (Tan, P-N., Steinbach, M., and Kumar, V., Introduction to Data Mining, Addison-Wesley, 2006). Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. Arvind. . Balasubramanian. arvind@utdallas.edu. Multimedia . Lab (ECSS 4.416). The University of Texas at Dallas. Me and My Research. Research Interests: . Machine Learning. Data Mining. Statistical Analysis. July 2011 373082J-01 Worldwide Technical Support and Product Informationni.comWorldwide Officesni.com/niglobal to access the branch office Web sites, which provide up-to-date support phone numbers, em Another Introduction to Data Mining. Course Information. 2. Knowledge Discovery in Data [and Data Mining] (KDD). Let us find something interesting!. Definition. := . “KDD is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” . OUTLINE. Data Analysis: Coding, editing, and tabulation of data. Application of various types of Graphs and charts.. Introduction to simple Descriptive analysis, and statistical analysis.. Types of reports, Importance of the Report and Presentation, Report format. Precautions in preparing reports. . Arvind. . Balasubramanian. arvind@utdallas.edu. Multimedia Lab. The University of Texas at Dallas. Me and My Research. Research Interests: . Machine Learning. Data Mining. Statistical Analysis. Applications of the above in Multimedia. Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. Agenda. Data Mining enabling Predictive Analysis. The Value of Predictive Analysis. SQL Server 2008 Predictive Analysis. Complete Predictive Analysis. Integrated Predictive Analysis. Extensible Predictive Analysis. Professor Tom . Fomby. Director. Richard B. Johnson Center for Economic Studies. Department of Economics. SMU. May 23, 2013. Big Data:. Many Observations on Many Variables . Data File. OBS No.. Target Var.. Instructor: . Yizhou. Sun. yzsun@ccs.neu.edu. January 6, 2013. Chapter 1. : Introduction. Course Information. Class . homepage: . http://. www.ccs.neu.edu/home/yzsun/classes/2013Spring_CS6220/index.htm. Credit: Gaby . Matalon. What is Data Mining?. The. . process . of analyzing data from different perspectives and summarizing it into useful information. It . uncovers patterns . in a large set of data. Course webpage:. http://www.cs.bu.edu/~. evimaria/cs565-11.html. Schedule: Mon – Wed, . 2:30-4:00. Instructor: . Evimaria. . Terzi. , . evimaria@cs.bu.edu. Office hours: . Tues. . 11. :00am-12:30pm. 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.

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