| Traditional association mining algorithms, like Apriori, generate all frequent itemsets existing within the dataset. However, only a very small fraction of this massive volume of frequent itemsets is interesting to the user. Therefore, such algorithms waste a lot of time and resources to uncover itemsets that are insignificant. The objective of this dissertation is to introduce data structure that can be used to support selective association mining, which is an association mining algorithm that generates only itemsets containing items of user interest. The first data structure introduced for selective association mining is itemset tree. The performance of itemset tree can be improved by reordering the items. Five different distributions are used to determine which of these distributions performed the best. Two of these distributions are extracted from the structure of a clustering algorithm known as UNIMEM. As the performance of the distributions from UNIMEM are evaluated, UNIMEM shows potential for selective association mining. However, the algorithm cannot be used directly and our proposed modified version of the conceptual tree of UNIMEM is called ISE-Tree. Experiments show that ISE-Tree performs better than itemset tree and we have successfully introduced a new improved data structure for selective association mining. |
