CSE 674: Introduction to Data Mining

Description

Knowledge discovery process, key data mining techniques, efficient high performance mining algorithms, exposure to applications of data mining (e.g., intrusion detection, text mining, bioinformatics).

Level, Credits, Class Time Distribution, Prerequisites

Level	Credits	Class Time Distribution	Prerequisites
UG	3	3 cl	670; 680; or permission of instructor

Quarters Offered, General Information, Exclusions, Cross-Listings, etc.

Objectives

Mastery of the knowledge discovery process
Mastery of key data mining techniques
Familiarity with underlying data structures and scalable implementations
Familiarity with applying said techniques in practical domains (e.g., bioinformatics and intrusion detection)

Texts

Data Mining: Concepts and Techniques, J. Han & M. Kamber, Morgan Kaufmann 2001
Data Mining: Introductory and Advanced Topics, M. Dunham, Prentice Hall, 2002

Topics

Number of Hours	Topic
3	Introduction to KDD process and basic statistics
3	Data preprocessing
6	Classification algorithms
6	Clustering algorithms
6	Scalable data mining algorithms and systems support, parallel algorithms, database integration, data locality issues (embedded topic, frequent pattern algorithms)
5	Scalable and parallel algorithms
5	Applications
2	Reviews and exams

Representative Lab Assignments

Assignment 1: Modeling Time Series
Assignment 2: Data Preprocessing of Text Data
Assignment 3: Automated Categorization/Classification of Text Documents
Assignment 4: Clustering Text Data
Assignment 5: Summarizing Clusters using Frequent Pattern Algorithms

Grading Plan

Homeworks and Lab Assignments	60%
Midterm	20%
Final	20%

Relationship to ABET Criterion 3

a	b	c	d	e	f	g	h	i	j	k
XXX	XXX	X	XX	XX	X	XX	X	X	X	XX

Relationship to CSE Program Objectives

1a	1b	1c	2a	2b	2c	3a	3b	4a	4b	5a	5b	5c
XX	XX	XX	XX	X	XX		X	XX	XX	XX	XX	X

Preparer Information and Date

Prepared by Srinivasan Parthasarathy
Last modified 11/29/2004