wiki: https://wiki.eecs.yorku.ca/course_archive/2014-15/F/4412/

Course Description: Data mining is computationally intelligent extraction of interesting, useful and previously unknown knowledge from large databases. It is a highly inter-disciplinary area representing the confluence of machine learning, statistics, database systems and high-performance computing. This course introduces the fundamental concepts of data mining. It provides an in-depth study on various data mining algorithms, models and applications. In particular, the course covers data pre-processing, association rule mining, sequential pattern mining, decision tree learning, decision rule learning, neural networks, clustering and their applications. The students are required to doprogramming assignments to gain hands-on experience with data mining.
Objectives (expected learning outcomes):

Learning objectives include:

• Knowledge of the terminology and concepts of data mining;
• Insight into the possibilities and fundamental limitations of dta mining;
• Insight into the relative advantages and disadvantages of major approaches to data mining;
• Understanding of the basic methods and techniques used in data mining;
• Skills in applying the basic methods and techniques to actual problems in data mining.

Classes Tues/Thurs 13:00-14:30 Lassonde 3033

Office Hours Wednesdays 12:00 or by appointment

Topics

• Course Introduction
• Part l: Data Mining Algorithms, Techniques and WEKA
• Part ll: Useful Machine Learning Techniques for Data Mining
• Part lll: Statistical methods: Probabilistic Inference
• Part IV: Applications and Data Mining Systems
• Part V: Course review – one day
• Part VI: Student Presentations

Class Materials

• 1. Many many class handouts.
• 2. Copies of Data Mining books.
• 3. Copies of many many relevant papers.
• 4. Many other notes.

References

Recommended Textbook 

* Jiawei Han, Micheline Kamber and Jian Pei, Data Mining – Concepts and Techniques. Morgan Kaufmann, Third Edition, 2011.

Reference Books

* Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison Wesley, 2006.
* Ian H. Witten and Eibe Frank, Data Mining – Practical Machine Learning Tools and Techniques (2nd Ed.), Morgan Kaufmann, 2005.
* S.M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998.
* Margaret H. Dunham, Data Mining – Introductory and Advanced Topics, Prentice Hall, 2003.

  Some conference/journal papers (will be posted over the semester).

Graduate students are expected to complete all of 4412 and attend all classes.