PPT-Overview of Data Mining and the KDD Process

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. Forecasting of Complex Time-Stamped Events. Yasuko Matsubara (Kyoto University), . Yasushi Sakurai (NTT), . Christos Faloutsos (CMU), . Tomoharu. Iwata (NTT), . Masatoshi Yoshikawa (Kyoto Univ.). KDD 2012. Extracting Optimal Quasi-Cliques with Quality . Guarantees. . Charalampos (Babis) E. Tsourakakis. charalampos.tsourakakis@aalto.fi. KDD 2013. John Stamper. Pittsburgh Science of Learning Center. Human-Computer Interaction Institute. Carnegie Mellon University. 4/8/2013. The Classroom of the Future. Which picture represents the “Classroom of the Future”?. Principles for Industrial Data Mining. Paper Authored By:. Menzies. & . Kocaganeli. – Lane . Dept. of CS/EE, WVU. Bird, Zimmerman, & Schulte – Microsoft Research. Presentation By: . Ebeid. 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” . Ryan . S.J.d. . Baker. PSLC Summer School 2012. Welcome to the EDM track!. On behalf of the track lead, John Stamper, and all of our colleagues. Educational Data Mining. “Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in.” . John . Stamper. Pittsburgh Science of Learning Center. Human-Computer Interaction Institute. Carnegie Mellon University. About me.. 2. EDM Data. What kinds of data can we collect?. What levels?. What is the right size for EDM discovery?. Extracting Optimal Quasi-Cliques with Quality . Guarantees. . Charalampos (Babis) E. Tsourakakis. charalampos.tsourakakis@aalto.fi. KDD 2013. www.pwc.com. TU/e, .  . September 17. th. , 2015. Zbigniew ‘Zibi’ Paszkiewicz, Ph.D.. Manager. System and Process Assurance. Data Assurance Group. zbigniew.paszkiewicz@be.pwc.com. Purpose. Process mining @ PwC. 8.0#)#"4+'(1").()'(8)+(9.#$',+.():#.;+5"5)8)&'('=)&'(')"$:4.8""%).#)1.(,#'1,.#%=)0%"#%).9),7")&'(')*+,").#)'(8.(")"4%")'::"'#+(6).(),7")*+,")',),7")+(;+,',+.().9)&'(').#).,7"#);+%+,.#%),.),7")*+,")+%) John E. Hopcroft, Tiancheng Lou, Jie Tang, and Liaoruo Wang. Detecting Community Kernels in Large Social Networks. ICDM Meletios Dimopoulos,. 1. Hang Quach,. 2. Maria-Victoria Mateos,. 3. Ola Landgren,. 4. Xavier Leleu,. 5. David Siegel,. 6. Katja Weisel,. 7. Maria Gavriatopoulou,. 8. Albert Oriol,. 9. Neil Rabin,. Head, Asst. Professor,. A.P.C. . Mahalaxmi. College for Women,. Thoothukudi. -628 002.. . Data Mining : . Introduction . to C. oncepts and Techniques. Module overview. Evolution of Database . REVIEWED BROAD-BASED BLACK ECONOMIC EMPOWERMENT CHARTER FOR THE SOUTH AFRICAN MINING AND MINERALS INDUSTRY, 2016 ("MINING CHARTER 3. "). PRESENTATION PREPARED FOR . SAIMM – RESPONSIBILITIES PLACED ON OEMs AND SERVICE PROVIDERS.