CSE 5243: Introduction to Data Mining (Au18, Tu/Th 9:35-10:55am, Baker Systems 136)

Instructor: Huan Sun

Teaching Assistants: Fang Zhou (zhou.1250)

Level and credits: U/G, 3

Prerequisites: Introduction to Databases, Introduction to Algorithms, or grad standing or permission of instructor

Office hours and locations (Instructor): Tue 11:00AM-12:15PM, Dreese Labs 699

Office hours and locations (TA): Fang Zhou @ DL190, 3:00PM-4:00PM on Tuesday

Description

Introduction to the knowledge discovery process, key data mining techniques, efficient high performance mining algorithms, exposure to applications of data mining.

Grading Plan (★★★★★Note: All the deadlines are 11:59PM (midnight) of the due dates. No late submissions!)

  • Participation: 10%
  • Homework: 50%
  • Midterm Exam: 20%
  • Final Exam: 20%
  • No course project

Textbooks

Jiawei Han, Micheline Kamber, and Jian Pei. Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011

Recommended books for reading:

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Course Syllabus and Schedule (To be updated later)

Week Date Topic Assignment Out Assignment Due Lecture Notes
1 08/21 NO CLASS Chapter 1 (Han et al.)
1 08/23 Class Outline / Introduction Chapter 1 (Han et al.)
2 08/28 Review of Basic Probability and Statistical Concepts; Data Preprocessing Chapter 2 & Chapter 3 (Han et al.)
2 08/30 Data & Data Preprocessing Assignment 1 Chapter 2 & Chapter 3 (Han et al.)
3 09/04 Classification:Basic Concepts/Methods Chapter 8 (Han et al.)
3 09/06 Classification:Basic Concepts/Methods
4 09/11 Classification:Basic Concepts/Methods Assignment 2 (programming) Assignment 1 Due
4 09/13 Classification:Basic Concepts/Methods
5 09/18 Classification:Advanced Methods Chapter 9 (Han et al.), Sample Midterm
5 09/20 Classification:Advanced Methods & HW#2 Brief Discussion/QA & Clustering: Basic Concepts/Methods Chapter 10 (Han et al.)
6 09/25 Clustering: Basic Concepts/Methods(pdf)(pptx)
6 09/27 Clustering: Basic Concepts/Methods(pdf)(pptx)
7 10/02 Clustering: Basic Concepts/Methods(pdf) Assignment 3 (programming) Assignment 2 Due
7 10/04 Homework Discussion + Lecture Review + Midterm Review
8 10/09 Midterm Exam
8 10/11 Autumn Break
9 10/16 Mining Frequent Patterns and Associations: Basic Concepts Frequent Pattern Mining (Chapter 6, Han et al.)
9 10/18 Mining Frequent Patterns and Associations: Basic Concepts
10 10/23 Mining Frequent Patterns and Associations: Basic Concepts Assignment 4 Assignment 3 Due
10 10/25 Mining Frequent Patterns and Associations: Basic Concepts Sequence Pattern Mining (chapter) (Zaki et al.)
11 10/30 Mining Frequent Patterns and Associations: Advanced Methods Advanced Pattern Mining (Chapter 7, Han et al.)
11 11/01 Mining Frequent Patterns and Associations: Advanced Methods
12 11/06 Mining Frequent Patterns and Associations: Advanced Methods Chapter 3 (Leskovec et al.)
12 11/08 Mining Frequent Patterns and Associations: Advanced Methods & Finding Similar Items: Locality-Sensitive Hashing
13 11/13 Finding Similar Items: Locality-Sensitive Hashing Assignment 5 (Programming) Assignment 4 Due
13 11/15 Introduction to Graphs Chapter 4: Graph Data (Zaki et al.)
14 11/20 Introduction to Graphs (& Final Exam Sample Problems)
14 11/22 Thanksgiving Break
15 11/27 Introduction to Information Retrieval
15 11/29 Guest Lecture by Dr. Ping Zhang
16 12/04 Review Session Assignment 5 Due
16 12/07 (Friday) Final Exam 8AM-9:45AM at Baker Systems 136
16 12/08

Course slides are partly adapted from similar courses offered by Prof. Jiawei Han in UIUC, Prof. Srinivasan Parthasarathy in OSU, Prof. Yizhou Sun in UCLA, and from books listed above.