A Survey on Scalable and Parallel High Utility Itemset Mining
Keywords:
frequent pattern mining, association rulesAbstract
Utility itemset mining aims to the discovery of itemsets or patterns with stimulating interest. While frequent itemset or pattern mining finds the interesting patterns based on the occurrence frequency of a pattern, utility itemset mining (UIM) is a further development in this field. It integrates the aspect of utility in some form like weight, cost, amount, profit or any other factor of interest. Utility mining is thus an objective-oriented approach, which aims to find the patterns with a high utility such as more profit or low cost/side-effect etc. However, utility mining is a complex process than frequency itemset mining (FIM) as anti-monotonicity property does not hold for the itemsets like as in FIM. Many algorithms have been developed to mine the itemsets with utility information in recent years. But most of them are not scalable for the nature of data with which we deal nowadays, called big data. This paper focuses on reviewing the recent advances in the field of high utility pattern mining for large datasets with scalable and parallel processing algorithms. The paper is concluded with open problems and future directions for research in the arena of big data.
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