CS 583 Fall 2017

CS 583 - Fall 2017

Data Mining and Text Mining

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 may be changed too)

  1. Introduction
  2. Data pre-processing
  3. Association rules and sequential patterns (Sections 2.1 - 2.7)
  4. Supervised learning (Classification) (Chapter 3)
  5. Unsupervised learning (Clustering) (Chapter 4)
  6. Semi-supervised learning (Sections 5.1.1, 5.1.2, 5.2.1 - 5.2.4)
  7. Information retrieval and Web search (Sections 6.1 - 6.6, and 6.8)
  8. Social network analysis (Sections 7.1 - 7.4)
  9. Sentiment analysis and opinion mining (Sections 11.1 - 11.6; check out my two books)
  10. Recommender systems and collaborative filtering (Section 12.4)
  11. Web data extraction (Sections 9.1 and 9.2)
  12. Information integration (Section 10.8)

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

Rules and Policies

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By Bing Liu, Aug 20, 2017