Semester 9

Course: Data Mining



Course Code: E11
Course Level: Undergratuate
Obligatory/Elective: Elective
Semester: 9
Division: Division of Computers
Group: Group A
ECTS Credits: 5
Hours Per Week: 4
Website: eclass.uowm.gr/courses/ICTE204/
Language: Greek
Content:

Introduction to Data Mining Techniques: data, problems, applications. Data preprocessing: cleaning, transformation, methods for dimension reduction. Clustering: introduction, distances, k-means, hierarchical clustering. Association Rules: problem definition, the a-priori algorithm, the FP-Growth algorithm, evaluation of association rules. Classification: introduction, decision trees, over-fitting, missing values, rule-based classifiers, k-nearest neighbors. Methods for finding associations in multi-dimensional data and relational data.

Learning Outcomes:

Data Mining FundamentalsPre-processing dataData Mining Techniques:

  • Classification
  • Clustering
  • Association Rules

Using Weka

Pre-requirements:

-

Teaching Methods:

Lectures and labs

Validation:

Assignment (40 of the total mark) and exams (60% of the total mark)

Suggested Books:
  • Michael Vazirgiannis, Chalkidi Maria, Mining knowledge from databases and the web, G. DARDANOS - DARDANOS K., 2005.
  • Tan Pang - Ning, Steinbach Michael, Kumar Vipin, Introduction to data mining, A. Tziola & Sons PUBLICATIONS, 2010.
  • Margaret H. Dunham, DATA MINING, NEW TECHNOLOGIES PUBLICATIONS Ltd., 2004.