CS 583 Fall 2007

CS 583 - Fall 2007

Data Mining and Text Mining

If the class is full, please contact Santhi Nannapaneni (santhi@cs.uic.edu) to put you in the waiting list.

Course Objective

This course has three objectives. First, to provide students with a sound basis in data mining tasks and techniques. Second, to ensure that students are able to read, and critically evaluate data mining research papers. Third, to ensue that students are able to implement and to use some of the important data mining and text mining algorithms.

Think and Ask!

If you have questions about any topic or assignment, DO ASK me or even your classmates for help, I am here to make the course undersdood. DO NOT delay your questions. There is no such thing as a stupid question. The only obstacle to learning is laziness.

General Information



Teaching materials

Topics (subject to change, slides will be changed too)

Introduction Slides
  1. Data pre-processing Slides
  2. Association rules and sequential patterns Slides
  3. Supervised learning (Classification) Slides
  4. Unsupervised learning (Clustering) Slides
  5. Post-processing: Are all the data mining results interesting? Slides
  6. Information retrieval and Web search Slides
  7. Partially supervised learning Slides
  8. Link analysis Slides
  9. Data extraction and information integration Slides
  10. Opinion mining and summarization Slides
  11. Summary

Projects - graded (you will demo your programs to me)

Rules and Policies

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By Bing Liu, May 12, 2007