CS412: An Introduction to Data Mining


Fall, 2020



Course Objective

Provide a comprehensive overview of the fundamental concepts and techniques of data mining.

·      Be able to understand the key concepts of data mining techniques, including data preprocessing, data warehousing and cube, frequent pattern mining, classification, clustering.

·      Be able to apply the key data mining techniques to realistic setting, evaluate and analyze the mining results.


Basic Information

Class meeting:  online, syllabus

Instructor: Hanghang Tong (htong@illinois.edu)


·      Jian Kang, jiank2@illinois.edu

·      Yu Zhang, yuz9@illinois.edu

·      Dawei Zhou, dzhou21@illinois.edu

·      Yao Zhou, yaozhou3@illinois.edu

Office hours:

[All CT time, all on Zooms.]

·      Hanghang Tong: 10-11am, Monday; 10-11pm Wednesday

·      Jian Kang: 10-11pm, Tuesday; 2-3pm, Thursday

·      Yu Zhang, 10-11pm, Monday, 11am-noon, Thursday

·      Dawei Zhou: 11am-noon, Tuesday; 4-5pm, Tuesday

·      Yao Zhou: 11am-noon, Wednesday; 3-4pm, Friday

Online resources:

·      Piazza: piazza.com/illinois/fall2020/cs412

·      Compass 2g


Schedule (Tentative, subject to slight adjustment)

·      Class Outline / Chapter 1: Introduction (week 1)

·      Chapter 2: Know Your Data (week 1 & 2)

·      Chapter 3: Data Preprocessing (week 2 & 3)

·      Chapter 4: Data Warehousing & OLAP (week 4)

·      Chapter 5: Data Cube Technology (week 4 & 5)

·      Chapter 6: Mining Frequent Patterns and Associations: Basic Concepts (week 5 & 6)

·      Chapter 7: Mining Frequent Patterns, Associations: Advanced Methods (week 7, 8 & 9)

·      Chapter 8: Classification: Basic Concepts (week 9, 10, 11)

·      Chapter 9: Classification: Advanced Methods (week 12 & 14)

·      Chapter 10: Cluster Analysis: Basic Concepts (week 14, 15)

·      Chapter 11: Deep Learning (week 16)


Coursework and Grading

·      Assignments, Programming Assignments, and Exams

o   Written Assignments: 15% (three homework assignments expected)

o   Programming assignments: 20% (two programming assignments expected)

o   Midterm exam: 30%

o   Final exam: 35%

·      For students taking 4th credit

o   For students registering 4 credits: 25%. The overall scores will be scaled proportionally

o   Group project: 2-3 members


Key Dates

·      Assignments

o   A1: Sep. 3rd out, Sep. 19th due

o   A2: Sep. 19th out, Oct. 8th due

o   A3: Oct. 8th out, Nov. 7th due

o   A4: Nov. 7th out, Dec. 10th due

o   A5: Nov. 14th out, Dec. 10th due

·      Exams

o   Mid-term: 9:30-11:00am, Oct. 20th, Tuesday

o   Final: 1:30pm-4:30pm, December 15th, Tuesday

·      Project (for students taking 4th credit)

o   Project proposal due: Sep. 24th

o   Mid-point report due: Oct. 22nd

o   Paper submission due: Dec. 3rd

o   Review submission due: Dec. 3rd



Required: Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques (3rd ed), Morgan Kaufmann, 2011 


·      Charu C. Aggarwal,  Data Mining: The Textbook, Springer, 2015

·      P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005 (2nd ed. 2016)

·      Mohammed J. Zaki and Wagner Meira Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, 2014


Statement on Mental Health

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Counseling Center: 217-333-3704, 610 East John Street Champaign, IL 61820

McKinley Health Center:217-333-2700, 1109 South Lincoln Avenue, Urbana, Illinois 61801