CAS764

CAS764 DATA MINING

 

Objectives:

  • To introduce the basic concepts and techniques of data mining.
  • To develop skills of using recent data mining software for solving problems.
  • To be aware of advanced concepts of data mining techniques and its applications in the knowledge discovery process.

 

 

Data Mining Techniques-Data Mining Process-Process with a typical set of data-Big Data-Visualization of data through data mining software.

Data Mining Methods as Tools - Memory-Based reasoning methods of Data Mining - Algorithms with prototypical data based on real applications.

Data Stream Mining, Mining Time Series, Text Mining, Data Stream Clustering, mining Big Data

Market Basket Analysis - Fuzzy Data Mining approaches - Fuzzy Decision Tree approaches Fuzzy Association Rule applications. Rough Sets - Support Vector Machines - Genetic algorithms.

Social Computing - Analysis -Graph Mining – Social Network Mining-Web Mining – Web Usage Mining-Privacy Preserving Data Mining-Recommender Systems.

 

References:

 

  1. David L. Olson and Dursun Delen, “Advanced Data Mining Techniques”, Springer, 2008.
  2. Charu C. Aggarwal and Haixun Wang, “Managing and Mining Graph Data”, Springer, 2010.
  3. Ian H. Witten, Eibe Frank and Mark A. Hall, “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann Publishers, 2011.
  4. Jiawei Han and Micheline Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publishers, 2006.
  5. Margaret H. Dunham, “Data Mining Introductory and Advanced Topics”, Prentice Hall, 2003.
  6. Anand Rajaraman and Jeff Ullman, “Mining of Massive Datasets”, Cambridge University Press, 2011.

 

           Outcomes:

         Students will be able to:

                      Understand the concepts and algorithms of data mining.

                      Apply data mining techniques for business intelligence.

                      Be aware of the privacy and security issues in data mining.