Data Mining - Clustering and Association

This course introduces basic concepts and methods of Data Mining with specific reference to Clustering and Association Rules. We present concept and purposes of cluster analysis, together with its’ main components. Partitioning, hierarchical, density based, and graph based clustering methods are described. Particular attention is devoted to; cluster validity measures and clustering validation. The last part of the course introduces association rule discovery. The concepts of association rule, frequent itemset, support and confidence are given. Furthermore, we give a brief description of the Apriori algorithm for frequent itemset generation, and introduce the concepts of maximal and closed frequent itemset. Finally, different criteria, for evaluating the quality of association patterns, are introduced.