Subject Description Form
Subject Code
ISE464
Subject Title
Data and Knowledge Mining
Credit Value
3
Level
4
Pre-requisite/Corequisite/Exclusion
Nil
Objectives
The subject will enable students to develop the ability to
Intended Learning
Outcomes
Subject Synopsis/
Indicative Syllabus
1.
describe the basic concepts and methods of Data and Knowledge Mining;
2.
identify the major applications of Data and Knowledge Mining as well as
the limitations of the various techniques;
3.
apply Data and Knowledge Mining techniques and tools in solving
business problems.
Upon completion of the subject, students will be able to
a.
understand the basic principles of Data and Knowledge Mining;
b.
understand the concepts of data warehousing and OLAP;
c.
apply the Data and Knowledge Mining tools to various
problems;
d.
select and use appropriate software for business applications.
1.
Introduction to Data and Knowledge Mining
business
Why Data and Knowledge Mining is needed; Knowledge representation
and mapping; Knowledge elicitation methods; Data and knowledge
repositories; Business semantics.
2.
Data Warehousing and OLAP
Introduction to data warehouse; Data warehouse components; Building
data warehouse; On-Line Analytical Processing (OLAP); Patterns and
models.
3.
Data Mining Techniques
Data preprocessing, Association; Classification; Clustering; Weka; Cases
studies drawn from industrial and business applications.
4.
Knowledge Mining Techniques
Analyze trends and patterns (Google trends); Search and present results
18.3.2014
(Clusty); Create a personal knowledge management system (Blogs, RSS);
Compare products and services offerings; Quantitative data analysis.
Teaching/Learning
Methodology
A mix of lectures, tutorials, and lab sessions is used to deliver the various
topics in this subject. Lectures are conducted to initiate students with the
concepts and techniques of Data and Knowledge Mining that are reinforced by
in-class exercises and assignments. Tutorial sessions will be used to illustrate
the practical problems and to conduct exercises on analysis of Data and
Knowledge Ming problems. Students are given the opportunity to gain handson experience on operating Data Mining tools during the laboratory sessions.
Assessment Methods
in Alignment with
Intended Learning
Outcomes
Specific assessment
methods/tasks
%
weighting
Intended subject learning outcomes to
be assessed
a
b
c

1. Project
35%


2. Lab exercise
35%


3. Presentation
15%


4. Quiz
15%


Total
100%
d



Continuous assessments consist of a project, lab exercises, presentation, and
quizzes that are designed to facilitate students to achieve intended learning
outcomes. Lab exercise, including a written report, is designed to encourage
students to acquire deep understanding of the relevant knowledge, practice in
order to enrich their hands-on experience with various software tools and for
the instructor to present the knowledge concepts, problem formation, and
problem solving clearly.
The integrated application oriented project is designed to enhance students’
ability to acquire the understanding and using different data mining and
knowledge discovery principles, techniques, tools to solve a real problem
through team work. Presentation is designed to facilitate students to show their
group performances on applied different techniques in the Data and Knowledge
mining workflow. The quizzes are designed to drive students to review how
comprehensively and correctly they have understood the knowledge concepts,
principles, and theories taught in the subject.
Student Study
Effort Expected
18.3.2014
Class contact:

Lecture

Tutorial/Case Study

Laboratory
2 hours/week for 6 weeks
12 Hrs.
1 hour/week for 6 weeks
6 Hrs.
3 hours/week for 7 weeks
21 Hrs.
Other student study effort:

Preparation for the lab reports, presentations and
quizzes
Total student study effort
Reading List and
References
18.3.2014
77 Hrs.
116 Hrs.
1.
Han JW, Kamber M & Pei J 2011, Data Mining: Concepts and
Techniques, 3rd edn, Morgan Kaufmann Publishers
2.
Tan, P 2014, Introduction to Data Mining, Addison Wesley
3.
Taniar D & Chen L 2011, Integrations of Data Warehousing, Data
Mining and Database Technologies: Innovative Approaches, IGI-Global
4.
Roiger, R and Geatz, M 2003, Data Mining: A Tutorial-Based Primer,
Addison Wesley
5.
Larose, D 2005, Discovering Knowledge in Data: An Introduction to
Data Mining, John Wiley & Sons