Efficient Strategy For Minning High Utility Quanitative Itemsets

Abstract

As an extension from the traditional methods of Pattern Mining, High-Utility Quantita tive Itemset Mining (HUQIM) has become an important research area that answers the ever-growing need for useful information from the copious pool of data in reality. Since the nature of this research category is to unveil all possible important patterns from a database and manage both quantities (internal utility) with prices (external utility) of their items, algorithms of HUQIM usually work with very large search spaces that could heavily affect execution time. A recently proposed algorithm called Fast High-Utility Quantitative Itemset Miner (FHUQI-Miner) has overcome these limits with its two novel pruning strategies to narrow down space and subsequently outperformed other algorithms that were created before. While these strategies have been demonstrated to enhance the new algorithm’s perfor mance compared to its predecessors, there were certain shortcomings that the algorithm still faced. The frst limitation was how the proposed strategies would not operate as efciently on dense datasets as it would on sparse datasets, deriving from the similar ity in structure of the transactions, and thus increasing the number of join operations in the progress. The second limitation was that the two newly proposed strategies of FHUQI-Miner was based of a pruning strategy that had been developed for quite some time ago, and up until now, there exists a number of later introduced strategies that were proven to be more efcient at pruning undesirable items. This was also stated by the research group in discussing future possible improvements to their proposed algorithm. Therefore, the aim of this thesis would be two-fold: to refne the proposed pruning strategies of the existing FHUQI-Miner algorithm and to attempt at a more efcient prun ing strategy adapted from existing concepts that were proven to be better. The outcomes of these studies would consist of two modifed versions of the original FHUQI-Miner algo rithm. The frst version will reduce the complexity of the base algorithm by uniting the two pruning strategies into one, given the Q-item notation theory. The second algorithm will introduce a novel adaptation of the transaction projection methodology to HUQIM and enhance the pruning abilities of the base algorithm. These resulted alternatives have been verifed to be capable of reducing the number of join operations compared to the base algorithm on both sparse and dense datasets as they effectively remove more unpromising items during the mining processes.