CSE 572 Data Mining

CSE 572 Data Mining Spring 2014

Goals: This course will introduce basic concepts, representative algorithms, and state-of-art techniques of data mining. With the rapid advance of computer and internet technologies, a plethora of data accumulates and presents many challenges of big data. Data will not turn into knowledge no matter how big it is and how long it is kept. Mining nuggets from data will help understand patterns buried in data and add values to what we are currently doing in many areas. Data mining is a process that finds the valuables among the mountains of data. We will review and examine the present techniques and theories behind them, and explore new and improved techniques for real world data mining applications. The course is arranged to encourage active class participation, creative thinking, practical problem solving, exploration of novel ideas, and hands-on project development among the participants. A course project on some specific aspect of this emerging field will be given to explore some in-depth issue(s) and gain unique data mining experience and insights.

This course consists of the presentations from the instructor and/or from the participants.

Course Line Number: 12945	Credit Hours: Three, Jan 13, 2014 - May 2, 2014
Class Schedule: TTh 12:00 - 1:15 pm	Classroom: BYAC 150
Instructor: Xia (Ben) Hu	Course Plan in Blackboard
Email: xia.hu at asu.edu Office: Brickyard 586
Office Hours: TTh 3:00 - 4:00 pm or by appointment	TA Office Hours: M W 3:00 - 4:00 pm at Centerpoint

Prerequisite: Introduction to Artificial Intelligence (CSE471 or CSE598) or Introduction to Database Management Systems (CSE412), some system development experience related to data engineering and handling, or consent from the instructor.

Topics:

Background knowledge and introduction to the need and current status of knowledge discovery and data mining
Classification
Preparing data for mining
Clustering
Evaluation and validation
Data mining applications
Mining association rules from large databases
Real-world applications and case studies

Homework: In addition to some regular homework exercises (assignments and quizzes), students are expected to participate in classroom discussions and Q&A.

Project: Students are expected to work on some programming projects. We will discuss the format in our first class. The evaluation of the project consists of progress report, project presentation and/or demonstration, and a written report.

Textbooks: There will also be research papers and other reference books (we will discuss about the following books and other pertinent issues. Be there and have all your questions answered in the first class).
    Introduction to Data mining
    Pang-Ning Tan, Michael Steinbach, and Vipin Kumar
    Addison Wesley,

    Data Mining: Concepts and Techniques
    Jiawei Han and Micheline Kamber
    Morgan Kaufmman Publishers,

    Data Mining: Practical Machine Learning Tools and Techniques with JAVA
    Ian H. Witten and Eibe Frank
    Morgan Kaufmman Publishers

Evaluation Methods (tentative): Homework assignments – 15%, Class participation and quizzes - (5%), Project – 20%, Exam(s) - 60%.

Academic Integrity and Student Conduct

Created on January 2, 2014.