CS525 DATA MINING COURSE INTRODUCTION

1. CS525 DATA MINING COURSE INTRODUCTION YÜCEL SAYGIN SABANCI UNIVERSITY

2. Contact Info • ysaygin@sabanciuniv.edu • http://people.sabanciuniv.edu/~ysaygin • Tel : 9576 • No Specific office hours. You can drop by anytime you like. Email or call me to make sure I am at the office.

3. Course Info • Reference Book:Data Mining Concepts and Techniques • Author: Jiawei Han and Micheline Kamber • Publisher: Morgan Kaufmann

4. Course Info • Grading: • Midterm : 30% (April 14-18) • Homework : 10% • Project : 30% • Paper presentation : 10% • Term Paper : 10% • Attendance during paper presentations: 10%

5. Topics that will be covered • Different Data Mining Techniques • Association Rules • Classification • Clustering • Data Mining and Security Issues • Applications of Data Mining • Data Warehousing

6. Aim of the course • Knowledge: • To introduce data mining concepts • Skills: • paper reading and presentation • research and/or project work

7. A Rough Schedule • March, April, First Week of May: • Lectures on various data mining techniques • Invited Speakers form Industry to share their experiences • Remaining 4 weeks: Paper presentations and discussions in class about research issues

8. What I will do • Give the basics on data mining • broad data mining concepts • research issues • Project supervision • Give directions and advise on the projects I proposed (will be provided in the next slides) • Coordination of the presentations

9. What I expect you to do • I expect you to do things wrt your background and expertise. • Students with CS background will do projects involving implementation and/or research • Others can do application projects • On a real application • That will involve data collection, cleaning etc • With at least two data mining tools that will be compared in terms of functionality for the chosen application

10. What I expect you to do • Understand the basic data mining concepts • Choose a specific area and two related papers on the same topic for presentation in class • Attendance is required for paper presentations and you will loose 2% of your overall for each presentation you missed. • Write a term paper on the two papers presented. • Do a project and a final report describing what you learned or achieved in the scope of the project.

11. Projects • Data Mining and Game Theory. Will be co-supervised with Ozgur Kibris from Economics (Mostly research, and survey, may involve algorithms design. Good for students in SLP) • Implementation of algorithms for data security against data mining methods (pure algorithms survey and implementation, good for CS students who like implementation)

12. Projects • Development of algorithms for protecting sensitive data against various data mining algorithms (research and implementation, good for CS students) • Hiding Sequential patterns in temporal data by changing time granularities is an example • Survey and Implementation of the existing Privacy preserving data mining methods (pure implementation, good for CS students)

13. Introduction to Data Mining • Why do we collect and process historical data? • What is the purpose of data mining? • What are the applications?

14. Introduction to Data Mining • Data is mostly stored in data warehouses • Data Mining Techniques are used to analyse the data: • Association rule finding from transactional data • Clustering of data with multiple dimensions • Classification of given data into predefined classes