Download SAK 5609 DATA MINING

SAK 5609
DATA MINING
Prof. Madya Dr. Md. Nasir bin
Sulaiman
[email protected]
03-89466514
Synopsis
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Kredit:
3(3+0)
Contact hours: 3 x 1 hour per week
Semester:
I
Emphasis on concepts of data mining. It includes
principles of data mining, data mining functions,
data mining processes, data mining techniques
such as K-nearest neighbour and clustering
algorithms, rule induction, decision tree
algorithms, association rule mining, neural
networks and genetic algorithms; and data mining
examples. Industrial and scientific applications
will be given.
Assessment & References
 Assessment:
– Exercises (10%)
– Project I (15%) + presentation I (5%) Week 7
Project II (15%) + presentation II (5%) Week 14
– Mid-exam 20% (1 hour) Week 6
– Final exam 30% (1.5 hours) Week 15 - 17
 References:
– Jiawei Han & Micheline Kamber, (2006), “Data Mining: Concepts
and Techniques”, 2nd. Ed., Morgan Kaufman.
– Michael J.A.Berry & Gordon S. Linoff, (2004), “Data Mining
Techniques (2nd edition)”, Wiley.
– Other related articles
Course Contents
 Chapter 1 Introduction
– Motivation
– Origin of data mining
– What it is/ isn’t
– The KDD process
– Types of data
 Chapter 2 Data mining tasks
– Classification
– Association rule mining
– Sequential pattern mining
– Clustering
– Anomaly detection
 Chapter 3 Data issues
– What is data set?
– Types of attributes
– Transformation for different types
– Types of data
• Structured data, record data, data matrix, document
data, transaction data, graph data, ordered data
– Data quality
• Noise and outliers, missing values,
inconsistent/duplicate data
 Chapter 4 Data preprocessing
– Why Data Preprocessing?
– Why Is Data Preprocessing Important?
– Major Tasks in Data Preprocessing
• Data Cleaning
• Data integration
• Data transformation
• Data reduction
• Data discretization
 Chapter 5 Association rule mining
– Introduction
– The Model
– Goal and Key Features
– Mining Algorithms
– Problems with the Association Rule Model
– Issues of association rules
– Other Main Works on Association Rules
 Chapter 6 Sequential Pattern Mining
– Sequence databases and pattern analysis
– Mining algorithms
– Challenges on sequential mining
– Studies on sequential mining
 Chapter 7 Classification and Prediction
– Classification Model
– General Approach
– Classification—A Two-Step Process
– Classification Techniques
– Evaluating classification methods
– Decision Tree Based Classification, rule based
classifiers, nearest neighbor classifiers etc
 Chapter 8 Clustering and Anomaly
– What is/is not cluster analysis?
– Examples of clustering applications
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Types of data in clustering analysis
Types of clustering – hierarchical, partitional
Major Clustering Techniques
Approaches to anomaly detection
Issues dealing with anomalies
 Chapter 9 Data Mining Applications