datamining.jpgCS 570 Introduction to Data Mining

Spring 2008

 

 

Lecture: TT 11:30-12:45pm MSC W303

Instructor: Li Xiong

Office Hours: TT 2-3pm MSC E412 or by appointment

Class Web: http://www.mathcs.emory.edu/~cs570000

Class Mailing List: cs570000-list@mathcs.emory.edu

 

 

Quick Links: Lecture Notes | Assignments | Project | Exam | Resources

 

Textbook:

 

Data Mining: Concepts and Techniques, 2nd Edition.  Jiawei Han and Micheline Kamber

 

Overview:

 

This course offers an introduction to data mining concepts and techniques. The goal is for students to have a solid foundation in data mining that allows them to apply data mining techniques to real-world problems and to conduct research and development in new data mining methods.  Topics include: data preprocessing, design and implementation of data warehouse and OLAP systems, data mining algorithms and methods including association analysis, classification, cluster analysis, as well as emerging applications and trends in data mining.

 

Prerequisites:

 

There are no prerequisites although some familiarity with a programming language, such as Java or C++, is required for programming assignments and/or final project.  Some knowledge about database systems and statistics will be helpful.

Tentative Syllabus

Week#

Date

Topics (Lecture Notes)

1

1/17

Chap 1. Introduction

2

1/22

Chap 2. Data Preprocessing: Descriptive Data Summarization, Data Cleaning

1/24

Chap 2. Data Preprocessing: Data Integration, Data Transformation

3

1/29

Chap 2. Data Preprocessing: Data Reduction, Data Discretization

1/31

Chap 5. Frequent Pattern Mining and Association Analysis

4

2/5

 

2/7

 

5

2/12

Chap 6. Classification and Prediction

2/14

 

6

2/19

 

2/21

 

7

2/26

Chap 7. Cluster Analysis

2/28

 

8

3/4

 

3/6

 

9

3/11

Spring Recess

3/13

Spring Recess

10

3/18

 

3/20

Midterm Exam

11

3/25

Chap 3 and 4. Data Warehousing and Cube Computation

3/27

 

12

4/1

 

4/3

Chap 8 and 9. Data Streams and Graph Mining

13

4/8

 

4/10

 

14

4/15

Chap 11. Applications and Trends

4/17

 

15

4/22

Student Project Workshop

4/24

Student Project Workshop

 

Assignments and Presentations:

 

There will be reading, written and programming assignments, spaced out over the first 2/3 of the semester (the last 1/3 of the semester is reserved for the final course project).  The typical time frame is one week for reading or written assignments and two weeks for programming assignments.  There will be also in-class paper presentations for selected papers.

 

Exam:

There will be one midterm exam (similar to written assignments) testing your conceptual understanding of the material.  There will be no final exam.

Project:

 

There will be a final course project. You are expected to propose your own project. Sample projects and ideas for projects will be discussed and posted.  Project deliverables include project proposal, in-class project presentation, project report, source code and executable package if applicable. 

 

Grading:

 

Component

Weight

Assignments and Presentations

40%

Midterm Exam

30%

Course Project

30%

 

Score

Grade

>= 93.3333

A

>= 90

A-

>= 86.6666

B+

>= 83.3333

B

>= 80

B-

Enjoy yourself and good luck!