Download Page 1 of 2 COMP 3110 Data Mining And Knowledge Discovery (3

Title (Units):
COMP 3110 Data Mining And Knowledge Discovery (3,2,1)
Course Aims:
To learn the latest development of knowledge discovery and data mining concepts and techniques.
Theories and algorithms for data mining and knowledge discovery will be introduced. Relevant
applications in specific domains such as medicine and heath care will be covered.
Prerequisite:
STAT 1210 Probability and Statistics
COMP 1210 Data Structures and Algorithms
COMP 1160 Database Management
Learning Outcomes (LOs):
Upon successful completion of this course, students should be able to:
No.
1
2
3
4
5
6
Learning Outcomes (LOs)
Knowledge
Identify and distinguish data mining applications from other IT applications
Describe data mining algorithms
Describe applicability of data mining
Professional Skill
Suggest appropriate solutions to data mining problems
Analyze data mining algorithms and techniques
Attitude
Build up team spirit in solving challenging data mining problems
Calendar Description:
This course is aimed at providing an overview of concepts and techniques in knowledge discovery
and data mining. Relevant applications in specific domains such as medicine and heath care will be
covered.
Assessment:
No.
1
2
Assessment
Methods
Weighting
Continuous
Assessment
40%
Examination
60%
Remarks
Assignments and Labs will be used to consolidate their knowledge and develop
their skills in data mining. Group project will further strengthen their
understanding and problem solving skills.
Final examination questions are designed to see how far students have achieved
their intended learning outcomes. Analysis based questions will be used to assess
the understanding of data mining problems. Problem solving questions will be
used to assess students’ ability in tackling applications in data mining.
Learning Outcomes and Weighting:
Content
I. Introduction to Data Mining
II. Data Mining Algorithms
III. Clustering
IV. Case Studies in Data Mining
Group Project
References:
LO No.
1
2,3,5,6
2,5
3,4,5,6
6
J. W. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2000.
V. Hristidis, Information Discovery on Electronic Health Records, Chapman & Hall/CRC Data
Mining and Knowledge Discovery, 2009.
D. Hand, H. Mannila and P. Smyth, Principles of Data Mining, MIT Press, 2001
G. Chang, M. J. Healey, J. A. M. McHugh and J. T. L. Wang, Mining the World Wide Web – An
Information Search Approach, Kluwer Academic Publishers, 2001.
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R. Kimball, R. Merz, The Data Webhouse ToolKit: Building the Web-enabled Data Warehouse,
John Wiley & Sons, 2000
Pan-Ning Tan, Michael Steinbach and Vapin Kumar, Introduction to Data Mining, Pearson
International Edition, 2006.
Course Content in Outline:
Topic
I.
Introduction to Data Mining
A. Overview of data mining
B. Data preparation for knowledge discovery
C. Data warehousing
D. Data visualization and exploration
II.
Data Mining Algorithms
A. Association rules
B. Apriori algorithms
C. Classification algorithms
D. Mining event sequences
E. Applications
III.
Clustering
A. Classical algorithms
B. Graph-based clustering
C. Advanced methods
D. Applications
IV.
Case Studies in Data Mining
A. Health Informatics
B. Related applications
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