Data Mining

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.

Data Mining FundamentalsPre-processing dataData Mining Techniques:

• Classification
• Clustering
• Association Rules

Using Weka

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Lectures and labs

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

• 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.