Event
Data Mining and Applications [SS202520375]
Lecturers
Organisation
- KIT-Fakultät für Wirtschaftswissenschaften
Part of
- Brick Data Mining and Applications | Industrial Engineering and Management (M.Sc.)
- Brick Data Mining and Applications | Industrial Engineering and Management (B.Sc.)
- Brick Data Mining and Applications | Economics Engineering (M.Sc.)
- Brick Data Mining and Applications | Economics Engineering (B.Sc.)
- Brick Data Mining and Applications | Information Systems (M.Sc.)
- Brick Data Mining and Applications | Information Systems (B.Sc.)
- Brick Data Mining and Applications | Information Engineering and Management (B.Sc.)
- Brick Data Mining and Applications | Information Engineering and Management (M.Sc.)
- Brick Data Mining and Applications | Economathematics (M.Sc.)
Literature
U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996 (order online from Amazon.com or from MIT Press).
Jiawei Han, Micheline Kamber, Data Mining : Concepts and Techniques, 2nd edition, Morgan Kaufmann, ISBN 1558609016 , 2006.
David J. Hand, Heikki Mannila and Padhraic Smyth, Principles of Data Mining , MIT Press, Fall 2000
Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Verlag, 2001.
Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Pearson Addison wesley (May, 2005). Hardcover: 769 pages. ISBN: 0321321367
Ripley, B.D. (1996) Pattern Recognition and Neural Networks, Cambridge: Cambridge University Press.
Ian Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition, Morgan Kaufmann, ISBN 0120884070 , 2005.
Note
Learning objectives:
Students
- know the definition of Data Mining
- are familiar with the CRISP-DM
- are familiar with the most important Data Mining Algorithms like Decision Tree, K-Means, Artificial Neural Networks, Association Rules, Regression Analysis
- will be able to use a DM-Tool
Content:
Part one: Data Mining:
What is Data Mining?; History of Data Mining; Conferences and Journals on Data Mining; Potential Applications; Data Mining Process; Business Understanding; Data Understanding; Data Preparation; Modeling; Evaluation; Deployment; Interdisciplinary aspects of Data Mining; Data Mining tasks; Data Mining Algorithms (Decision Trees, Association Rules, Regression, Clustering, Neural Networks); Fuzzy Mining; OLAP and Data Warehouse; Data Mining Tools; Trends in Data Mining
Part two: Examples of application of Data Mining
Success parameters of Data Mining Projects; Application in industry; Application in Commerce
Workload:
Total workload for 4.5 CP: approx. 135 hours
Attendance: 30 hours
Preparation and follow-up: 65 hours
Exam preparation: 40 hours
Exam preparation: 40 hours