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 ([email protected])
TAs:
·
Jian
Kang, [email protected]
·
Yu
Zhang, [email protected]
·
Dawei
Zhou, [email protected]
·
Yao
Zhou, [email protected]
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
Textbooks
Required: Jiawei Han, Micheline Kamber and
Jian Pei, Data Mining: Concepts and Techniques (3rd ed), Morgan
Kaufmann, 2011
Reference:
·
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
Diminished mental
health, including significant stress, mood changes, excessive worry,
substance/alcohol abuse, or problems with eating and/or sleeping can interfere
with optimal academic performance, social development, and emotional wellbeing.
The University of Illinois offers a variety of confidential services including
individual and group counseling, crisis intervention, psychiatric services, and
specialized screenings at no additional cost. If you or someone you know
experiences any of the above mental health concerns, it is strongly encouraged
to contact or visit any of the University’s resources provided below. Getting help
is a smart and courageous thing to do -- for yourself and for those who care
about you.
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