Download CS4412 Data Mining - Kennesaw State University

CS4412 Data Mining
(Fall 2016)
College
Department
Program
Course Prefix and Number
Course Title
Credit Hours
Prerequisite(s)
Area
Course Description
Learning Objectives for this
course
Contribution of the course
to the program outcomes
Assessment plan and
process
College of Computing and Software Engineering
Computer Science
BSCS
CS4412
Data Mining
3
CS3304 & CS3310
__Area F __ Major Required _x_ Major Elective
This course covers fundamental data mining concepts and techniques for
discovering interesting patterns from data in various applications.
Topics include data preprocessing, data warehousing and OLAP, mining
frequent patterns, classification, clustering, and tend analysis.
Upon the completion of this course, students should be able to
1. explain fundamental data mining concepts and techniques
2. implement typical data mining algorithms
3. apply data mining techniques to solve real life problem
4. design and construct data warehouse
1. Demonstrate an advanced understanding of the capabilities and limits of
computation, hardware and software systems, and software development
2. Analyze complex problems in the computing discipline and design
solutions that integrate hardware and software, and that are technologically
appropriate and theoretically sound
Evaluation will be through exams, homework assignments and course
projects. Evaluation will consist of:
Midterm Exam:
25%
Final Exam:
25%
Homework assignments:
25%
Course project
25%
100%
Instruction Delivery
Method
_x_ Traditional On Campus
_x_ Fully Online
__ Quality Matters Approved
__ Hybrid (describe)
Proposal Lead Author
Funding Required
Lab Fees or special tuition
Ying Xie
NA
NA
CS 4412 Data Mining
3 Class Hours, 0 Laboratory Hours, 3 Credit Hours
Course Description: This course covers fundamental data mining concepts and techniques for
discovering interesting patterns from data in various applications. Topics include data preprocessing, data
warehousing and OLAP, mining frequent patterns, classification, clustering, and tend analysis.
Instructor: Dr. Ying Xie
Office: J360
Email: [email protected]
Office hours: Tuesday and Thursday 1:30pm-3:30pm, 6:30pm – 7:30pm
Learning Objectives:
Upon the completion of this course, students should be able to
1. explain fundamental data mining concepts and techniques
2. implement typical data mining algorithms
3. apply data mining techniques to solve real life problem
4. design and construct data warehouse
Textbook and Learning Materials:
1. Jiawei Han, Micheline Kamber, Data Mining: Concepts and Techniques; 3/e, ISBN 0123814790,
2011
2. Online Materials
Instructional Delivery Methods and Attendance Policy: This course will have in-classroom
lectures and also provide both synchronous and asynchronous distance learning options.
Course Requirements and Assignments: Students will be expected to attend all classes in
classroom or through distance learning delivery, work on homework assignments and course project,
and take all exams.
Evaluation and Grading: Evaluation will be through exams, homework assignments and course
projects. Evaluation will consist of:
Midterm Exam:
Final Exam:
25%
25%
Homework assignments:
Course project
25%
25%
100%
Academic Honesty Statement: Every KSU student is responsible for upholding the provisions of the
Student Code of Conduct, as published in the Undergraduate and Graduate Catalogs. Section II of the
Student Code of Conduct addresses the University's policy on academic honesty, including provisions
regarding
plagiarism
and
cheating,
unauthorized
access
to
University
materials,
misrepresentation/falsification of University records or academic work, malicious removal, retention, or
destruction of library materials, malicious/intentional misuse of computer facilities and/or services, and
misuse of student identification cards. Incidents of alleged academic misconduct will be handled through
the established procedures of the University Judiciary Program, which includes either an "informal"
resolution by a faculty member, resulting in a grade adjustment, or a formal hearing procedure, which
may subject a student to the Code of Conduct's minimum one semester suspension requirement.
Students are encouraged to study together and to work together on course projects as per the instructor’s
specifications; however, the provisions of the STUDENT CONDUCT REGULATIONS, II. Academic
Honesty, KSC Undergraduate Catalog will be strictly enforced in this class.
Students are required to work INDEPENDANTLY on homework assignments and online exams.
Schedule and Topic Coverage:
Week
1
2
Lecture Topic
Reference
Introduction
Chapter 1
Data Preprocessing
Data Warehousing
OLAP
Chapter 2, 3
Chapter 4
Chapter 5
Chapter 6
Chapter 6
Chapter 7
Exam
Chapter 8
11
12
13
14
Mining Frequent Patterns
Correlation Mining
Advanced Pattern Mining
Midterm Exam
Classification
Classification: Advanced Method
1
Classification: Advanced Method
2
Clustering Analysis
Advanced Clustering Analysis 1
Advanced Clustering Analysis 2
Trend Analysis
16
Final Exam
3
4
5
6
7
8
9
10
10
Chapter 9
Chapter 9
Chapter 10
Chapter 11
Chapter 11
Chapter 13
As per Semester
Schedule
Important Dates:
September 3-5: Labor Day Break
October 5: Last Day to Withdraw Without Academic Penalty
Nov. 21-27: Fall Break
December 5: Last Day of Class
University Policy on Accommodating Students with Disabilities
Students requesting accommodation for disabilities must first register with the Office of Disabled
Student Support Services at http://www.kennesaw.edu/stu_dev/dsss/dsss.html. The Office of Disabled
Student Support Services will provide documentation to the student who must then provide this
documentation to the instructor when requesting accommodation. You must submit this documentation
prior to submitting assignments or taking the quizzes or exams. Accommodations are not retroactive,
therefore, students should contact the office as soon as possible in the term for which they are seeking
accommodations.