Courses offered in English for incoming Erasmus students
The Business School
INTERNATIONAL RELATIONS OFFICE
Agios Loukas, 654 04, Kavala Greece
0030-2510-462149
1
Responsible for the course (lecturer)
(name, phone number, e-mail address)
Title of the Course
ECTS credits
MSc Stavros Valsamidis
00 30 2510 462370 svalsam@teikav.edu.gr
Databases and data mining
5
Short contents of the course
Aim of the course and target audience
1.
Database systems concepts
2.
Relational model ERD
3.
Normalization at 4NFs
4.
Classification and regression rules
5.
Clustering
6.
Association rules
7.
Web mining
The course will introduce students to the database systems and data mining techniques.
In the 1st part, the students will
 understand the concepts and terms of the data base analysis, design and some implementations.
 recognize the importance of data base analysis and design in the implementation of any data base application.
 understand the structural constraints of relationships and how to perform them.
 understand the types of attributes, primary keys, foreign keys, super keys … etc.
 understand the process drawing the ER-Diagrams.
 understand carefully how to perform the normalization process of relations and then producing the final ER-
Diagram of any database application before implementation.
In the 2nd part, the students will
 be introduced to the basic concepts and techniques of data mining.
 develop skills of using recent data mining software for solving practical problems.
 use web mining techniques for knowledge discovery .
1 Could be easily used and offered for TS movement to our Erasmus partners

Courses offered in English for incoming Erasmus students
Teaching Methods duration and
Evaluation
Offered Period
Indicative bibliography
Target audience: Undergraduate or Master students (2nd part)
In class presentations combined with laboratory (PCs) use.
30 hours
50% course works
50% final tests
Spring semester
1.
Database System Concepts, Silberschatz, Korth and
Sudarshan, Mc Graw-Hill, 5th Edition
2.
Database Systems, Thomas Connolly Carolyn Begg,
Addison-Wesley 4th Edition
3.
Database Management Systems, Ramakrishnan &
Gehrke, Mc Graw-Hill, 3rd Edition
4.
Jiawei Han, Micheline Kamber, Data Mining : Concepts and Techniques, 2nd edition, Morgan Kaufmann, 2006.
5.
Ian Witten and Eibe Frank, Data Mining: Practical
Machine Learning Tools and Techniques, 2nd Edition,
Morgan Kaufmann, ISBN 0120884070, 2005.
6.
Bing Liu, "Web Data Mining: Exploring Hyperlinks,
Contents and Usage Data", Springer, 2007
7.
Valsamidis, S. (2008), Data Mining (handnotes)