An Efficient Data Mining Algorithm by Multi-Utility Minimum Support and Prefix-Search Strategy

Abstract

In recent years, how to discover the valuable knowledge from a huge amount of data has been a hot topic. Data mining is one of the solutions for this topic. Actually, data mining has been studied for a long time, including a lot of paradigms. Among these paradigms, High-Utility Itemset Mining attracts much research attention because it can find the itemsets different from traditional frequent itemsets. Although these related works have been shown to be efficient, it still cannot mine the really rare itemsets infrequent by only one minimum utility support. In addition, an efficient mining algorithm relies on an important factor “search strategy”. For these concerns, in this paper, an efficient high-utility itemset mining algorithm with multiple minimum utility support and prefix-search strategy is proposed to effectively mine the really valuable itemsets. For effectiveness, the rare but infrequent itemsets can be discovered by the individually specified utility supports. For efficiency, the aimed itemsets can be mined without the level-wise searching by a prefix-search way. The experimental results show the proposed algorithm performs better than the compared one on the synthetic and real datasets.