This course aims to provide basic knowledge on data mining discipline and experience on the techniques used for data analysis.

TOPICS

1.    Introduction

2.    Principles of Data Mining

3.    Data, Exploring data

4.    Decision tree induction for Classification

5.    Other classification techniques

6.    Basics of cluster detection

7.    Association rules

8.    Anomaly detection

9.    Data mining applications, trends

TEXT BOOKS:

Introduction to Data Mining , Tan,Steinbach, Kumar, Addison-Wesley, 2006, ISBN-10: 0321321367 ISBN-13: 978032132136797803213213670321321367.

Data Mining Techniques and Applications, Hongbo Du, Cengage Learning, 2010, ISBN-13: 9781844808915 / ISBN-10: 1844808912

REFERENCES:

Data Mining: Practical Machine Learning Tools and Techniques, Witten and Eibe, Morgan Kaufmann.

Data Mining: Concepts and Techniques, Jiawei Han and Micheline Kamber, The Morgan Kaufmann Series in Data Management Systems, 2011.

SOFTWARE:

WEKA Waikato Environment for Knowledge Analysis Tool

Collection of state-of-the-art machine learning algorithms and data processing tools implemented in Java.

GRADING:

Midterm (2 Quizzes)40%

Assignments  25%

Project   35%