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Data Mining 2016 Martti Juhola 1 Lectures and exercises Martti Juhola: - advanced study course 5 ECTS - lectures on the 12th January in Pinni B 3107, then in Pinni B1084, on Tuesday at 10 – 12, from the 19th January to the 16th February, and on the 13th January in Pinni 1097, then in Pinni B1084, on Wednesday at 10 – 12, from the 20th January to the 17th February - 12 lectures, 24 h Jyrki Rasku: - weekly exercises in Pinni B0039, on Thursday at 12 – 14, from the 21st January to the 18th February - 5 times, 10 h 2 To pass the course: (1) at least 30% of weekly exercises made; if made more, additional scores can be obtained when one makes more of all weekly exercises than 30 %, additional scores [0,5] are given as follows: 30 %, 0; 41 %, 1; 52 %, 2; 63 %, 3; 74 %, 4; 85 %, 5 scores (at least 30% of all weekly exercises have to completed) (2) the examination is passed, when scores are obtained from [12,30]; exercise scores are added to the examination scores. Examinations: the 17th March and 7th April, 2016, at 16-20, in D10a+b. 3 Literature: Dorian Pyle: Data Preparation for Data Mining, Morgan Kaufmann (Elsevier), 1999 David Hand, Heikki Mannila and Padhraic Smyth: Principles of Data Mining, MIT Press, 2001 Margaret H. Dunham: Data Mining, Introductory and Advanced Topics, Pearson Education Inc., 2003 Ian H. Witten, Eibe Frank, Mark A. Hall: Data Mining, Practical Machine Learning Tools and Techniques (third edition), 2011 Krysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski and Lukasz A. Kurgan: Data Mining, A Knowledge Discovery Approach, Springer, 2007 Some journal articles. 4 Notes The content of the current course considers mostly preparation or preprocessing of data for mining for two reasons. Preprocessing is in a significant role to obtain as good results as possible in data mining. Machine learning algorithms needed for explorative data analysis, prediction, classification and clustering have a minor role in this course, beause they will be the content of course Machine Learning Algorithms (period IV, 2016) and probably that of Neurocomputing (spring term 2017). Since this is the first presentation of the current course, some appear. may 5