A study of selected itemset mining and association rule mining algorithms

Abstract

Data Mining is a central step in Knowledge Discovery in Databases (KDD). This thesis is within the context of Data Mining and more specifically revolves around a couple the Data Mining tasks: Itemset Mining and Association Rule Mining. In this thesis, the aim was to investigate a selection of Itemset Mining algorithms as well as Association Rule Mining. For Frequent Itemset Mining, I studied Apriori, its variant Apriori-Close, and Eclat. I also brought attention to Rare Itemset Mining and investigated Apriori-Rare. I discussed the simple version of Association Rule Mining and tested it using both Apriori and Eclat. Lastly, I talked a about the software I developed in order to test these algorithms.