CS 583 Fall 2020

CS 583 - Fall 2020

Data Mining and Text Mining

Taught Online Asynchronously
Using Blackboard and Zoom

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, TA or your classmates for help, I am here to make the course understood. DO NOT delay your questions. There is no such thing as a stupid question. The only obstacle to learning is laziness.

Online Platforms

We will mainly use Blackboard and Zoom for teaching and learning. Please get yourself familarized with the systems.

General Information

  • Instructor: Bing Liu
  • Teaching Assistant (TA)
    • Sections 1 and 2: Sepideh Esmaeilpour
    • Email: sesmae2@uic.edu

Section 1

  • Course Call Number: 30286
  • Lecture time slots: 12:30 - 1:45pm Tue & Thu
  • Instructor office hours: 10:00am-11:30am Tue
  • TA office hour: 4:00pm-5:00pm Thu
  • Office hours will be conducted on Zoom

Section 2

  • Course Call Number: 45283
  • Lecture time slots: 2:00pm - 3:15pm Tue & Thu
  • Instructor office hours: 10:00am-11:30am Tue
  • TA office hour: 4:00pm-5:00pm Thu
  • Office hours will be conducted on Zoom


  • Quizzes: 30%
  • No Midterm. It is replaced by
  • No Final Exam: It is replaced by
  • Assignments and the research project are done in groups of 2. Discussions with other students are allowed, but each group has to write your own code.


    Teaching materials

    Topics (subject to change; the reading list follows each chapter title)

    1. Introduction
    2. Data pre-processing
      • Data cleaning
      • Data transformation
      • Data reduction
      • Discretization
    3. Association rules and sequential patterns (Sections 2.1 - 2.7)
      • Basic concepts
      • Apriori Algorithm
      • Mining association rules with multiple minimum supports
      • Mining class association rules
      • Sequetial pattern mining
      • Summary
    4. Supervised learning (Classification) (Chapter 3)
      • Basic concepts
      • Decision trees
      • Classifier evaluation
      • Rule induction
      • Classification based on association rules
      • Naive-Bayesian learning
      • Naive-Bayesian learning for text classification
      • Support vector machines
      • K-nearest neighbor
      • Bagging and boosting
      • Summary
    5. Unsupervised learning (Clustering) (Chapter 4)
      • Basic concepts
      • K-means algorithm
      • Representation of clusters
      • Hierarchical clustering
      • Distance functions
      • Data standardization
      • Handling mixed attributes
      • Which clustering algorithm to use?
      • Cluster evaluation
      • Discovering holes and data regions
      • Summary
    6. Information retrieval and Web search (Sections 6.1 - 6.6, and 6.8)
      • Basic text processing and representation
      • Cosine similarity
      • Relevance feedback and Rocchio algorithm
    7. Semi-supervised learning (Sections 5.1.1, 5.1.2, 5.2.1 - 5.2.4)
      • LU learning: Learning from labeled and unlabeled examples
        • Learning from labeled and unlabeled examples using EM
        • Learning from labeled and unlabeled examples using co-training
      • PU learning: Learning from positive and unlabeled examples
    8. Social network analysis (Sections 7.1 - 7.4)
      • Centrality and prestige
      • Citation analysis: co-citation and bibliographic coupling
      • The PageRank algoithm (of Google)
      • The HITS algorithm: authorities and hubs
      • Mining communities on the Web
    9. Sentiment analysis and opinion mining (Sections 11.1 - 11.6; check out my two books)
      • Opinion mining problem
      • Document-level Sentiment classification
      • Sentence-level subjectivity and sentiment classification
      • Aspect-level sentiment analysis
      • Mining comparative opinions
      • Opinion lexicon generation
    10. Recommender systems and collaborative filtering (Section 12.4)
      • Content-based recommendation
      • Collaborative filtering based recommendation
        • K-nearest neighbor
        • Association rules
        • Matrix factorization
    11. Lifelong and continual learning
      • Introduction to lifelong learning?
      • Open-world learning
      • ON-the-job learning
      • Lifelong and continual learning chatbots

    Rules and Policies

    UIC Conunseling Center

    We value your mental health and emotional wellness as part of the UIC student experience. The UIC Counseling Center offers an array of services to provide additional support throughout your time at UIC, including workshops, peer support groups, counseling, self-help tools, and initial consultations to speak to a mental health counselor about your concerns. Please visit the Counseling Center website for more information (https://counseling.uic.edu/). Further, if you think emotional concerns may be impacting your academic success, please contact your faculty and academic advisers to create a plan to stay on track.

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    By Bing Liu, Aug 15, 2020