Download Course number: PO6017 Course title: Data Mining Required course

Course number:
PO6017
Required course or Elective course:
Required
Course hours:
Credits:
48
3
Type of Assessment:
In-class quiz 20%
Assignment 40%
Group project report and presentation 40%
Prerequisites / Corequisites:
None
Teacher:
General course objectives / course profile
This course aims to:
Course title:
Data Mining
Schedule:
Spring semester
Form of course:
Lectures
Language of instruction
English
It is an introduction to the field of data mining (also known as knowledge discovery from data, or KDD
for short).
It focuses on fundamental data mining concepts and techniques for discovering interesting patterns
from data in various applications
It emphasizes techniques for developing effective, efficient, and scalable data mining tools.
Learning outcomes:
After completion of this course the student should be able to:
• Understand what Is Data Mining, what kinds of data can be mined, what kinds of patterns can
be mined, and what kinds of applications are targeted.
• Explain major Issues in data mining.
• Applymachine learning, pattern recognition, statistics, visualization, algorithm, database
technology and high-performance computing in data mining applications.
• Identify what kinds of technologies are used for different application.
• Manipulate data preprocessing, data Warehouse and OLAP technology, data cube technology;
mining frequent patterns andassociation, classification, clustering, and outlier detection.
Content:
Getting to Know Your Data; Data Preprocessing; Data Warehouse and OLAP Technology: An
Introduction; Advanced Data Cube Technology;
Mining Frequent Patterns & Association: Basic Concepts;
Mining Frequent Patterns & Association: Advanced Methods;
Classification: Basic Concepts ; Classification: Advanced Methods;
Cluster Analysis: Basic Concepts; Cluster Analysis: Advanced Methods;
Outlier Analysis.
Teaching materials and reference books:
Data Mining: Concepts and Techniques (Third Edition).Jiawei Han, MichelineKamber, Jian Pei. Morgan
Kaufmann, 2012.