Knowledge Discovery in Databases

COURSE INFORMATION

COURSE NAME

Knowledge Discovery in Databases

Course Code

 

Course Type

Optional

Level of Course

Undergraduate

Year of Study

4th

Term

Spring

ECTS Credits

5

Name of Instructor

Georgios Evangelidis

E-mail

gevan@uom.gr

Office Hours

Mondays and Wednesdays, 10am to 12pm

In-Classroom Study

3 hours per week

Out-of-Classroom Study

1 hour per week

Objective of the Course

To learn and practice on a number of important data mining algorithms (association rules, classification, clustering) that can be used to discover knowledge from data. To learn and practice on-line analytical processing techniques on data.

Prerequisites

None

Course Contents

Data Warehousing - OLAP - Data Mining concepts and techniques - Classification - Clustering - Association Rules - Time Series Data Mining.

Recommended Readings

P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Addison Wesley, 2006.

M.H. Dunham, Data Mining: Introductory and Advanced Topics, Pearson Eductions, 2003.

Richard Roiger & Michael Geatz, Data Mining: A Tutorial Based Primer, 2002.

Teaching Methods

Seminar + student presentations

Assesment Methods

50% Homework and Presentation, 50% Final exam

Language of Instruction

English

Course Schedule

 

1. Week

Data Warehousing - OLAP

2. Week

Classification

3. Week

Clustering

4. Week

Association Rules

5. Week

Time Series Data Mining

6. Week

Student Presentations

7. Week

Student Presentations

8. Week

Student Presentations

9. Week

Student Presentations

10.Week

Student Presentations

11.Week

Student Presentations

12.Week

Student Presentations