Teaching » CS 525 Data Mining

GENERAL INFORMATION

Instructor: Yücel Saygin
Office: FENS 2081
E-mail: ysaygin@sabanciuniv.edu
Tel: 9576
Web: http://people.sabanciuniv.edu/ysaygin/
 
Text Book: Data Mining Concepts and Techniques
Authors: Jiawei Han & Micheline Kamber
Publisher: Morgan Kaufmann

ANNOUNCEMENTS
  • HW1 and sample data set:
HOMEWORKS
  • HW1 (5%)
PROJECTS

TBA

TERM PAPER

Choose two or more papers from the following sources. You should consult me during the paper selection process.
  • Conferences SIGKDD, SIGMOD, ICDE, VLDB
  • Journals IEEE TKDE, KDD Journal, ACM TODS, ACM TOIS, VLDB Journal, or a journal in your area of expertise (such as bioinformatics, image processing ...).
  • You may use the following link to browse the contents of the listed conferences and journals: DBLP
LECTURE NOTES
  • Course Introduction
  • Lecture 1
  • Lecture 2
  • Lecture 3
  • Lecture 4
  • Lecture 5
  • Lecture 6
GRADING
  • Midterm : 30%
  • Homework : 10%
  • Project : 30%
  • Paper presentation : 10%
  • Term Paper : 10%
  • Attendance during paper presentations: 10%
SOME USEFUL LINKS SYLLABUS

In this course we will cover data mining concepts and data warehousing with the following order:

Week 1
  • Introduction to data mining and data warehousing
Week 2
  • Introduction to relational data model and query languages
Week 3
  • Data warehousing and OLAP technology
Week 4
  • Data preparation
Week 5
  • Association rule mining
Week 6
  • Data mining and data confidentiality
Week 7
  • Classification and prediction
Week 8
  • Clustering
Week 9
  • Mining complex types of data