Data Mining is the process of extracting knowledge from data. The main topics covered in this course include pre-processing, sorting, grouping, membership rules, anomaly, and the data mining process itself. The discipline aims to provide students with the fundamental skills needed to conduct their own research in data mining.
- Mohammed J. Zaki and Wagner Meira Jr. Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, May 2014.
- Ian H. Witten, Eibe Frank, and Mark A. Hall. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington, MA, 3 edition, January 2011.
- Jiawei Han, Micheline Kamber, and Jian Pei. Data Mining: Concepts and Techniques. Morgan Kaufmann, Waltham, Mass., 3 edition, July 2011.
- Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning: with Applications in R . Springer, 1st edition, August 2013.
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2nd edition, April 2011.
- Bing Liu. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer, softcover reprint of hardcover 2nd ed. 2011 edition, August 2013.
This course is regularly offered once a year at CEFET/RJ for graduate students (PPCIC and PPPRO).
Slides and schedule available at Moodle.