SAK 5609DATA MINING

Prof. Madya Dr. Md. Nasir bin Sulaiman

nasir@fsktm.upm.edu.my

03-89466514

Synopsis 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– 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