Content (Syllabus outline)
1. Introduction to data mining.
2. Use cases of data mining.
3. Process of knowledge from data discovery, data mining.
4. Input: Concepts, instances, attributes.
5. Output: Knowledge Representation.
6. CRISP-DM standard.
7. Data preparation.
8. Data visualization.
9. Machine learning (Supervized learning, Unsupervized learning, Semi-supervized learning, Deep Learning)
10. Algorithms: Decision tree, Classification, Regression, Instance based algorithms, Naive Bayes, neural networks, clustering, genetic algorithms.
11. Model evaluation and Implementation.
12. Overview of data mining tools (Orange, Weka, R).
Intended learning outcomes - transferable/key skills and other attributes
- To prepare the data from the available sources.
- To choose the appropriate data mining method, do the data mining process, and critically evaluate and interpret the solution.
Readings
Temeljna literatura:
1. Provost, F. & Fawcett, T. (2013): Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking 1st Edition, O'Reilly
2. Zaki, M. J., Wagner, M. Jr.: Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, New York, 2014. (izbrana poglavja)
3. Leskovec, J., Rajaraman, A., Ullman, J. D.: Mining of Massive Datasets. Cambridge University Press, New York, 2014. (izbrana poglavja)
4. Kononenko, I., Robnik Šikonja, M.: Inteligentni sistemi. Založba FE in FRI, Ljubljana, 2010.
5. Bramer, M.A. Principles of data mining. London : Springer, 2007.
6. Han, J., Kamber, M. Data mining, concepts and techniques, 3nd edition. Morgan Kaufmann Publishers, Elsevier, San Francisco, 2012.
7. Tan, P.N., Steinbach, M., Kumar, V. Introduction to data mining. Boston, Pearson Addison Wesley, 2006.
8. Witten, I.H., Frank, E.: Data mining Practical Machine Learning Tools and Techniques, Elsevier, 2005.
