A weighted frequent itemset mining algorithm for intelligent decision in smart systems

Abstract

Intelligent decision is the key technology of smart systems. Data mining technology has been playing an increasingly important role in decision-making activities. Frequent itemset mining (FIM), as an important step of association rule analysis, is becoming one of the most important research fields in data mining. Weighted FIM in uncertain databases should take both existential probability and importance of items into account in order to find frequent itemsets of great importance to users. However, the introduction of weight makes the weighted frequent itemsets not satisfy the downward closure property any longer. As a result, the search space of frequent itemsets cannot be narrowed according to downward closure property which leads to a poor time efficiency. In this paper, the weight judgment downward closure property for the weighted frequent itemsets and the existence property of weighted frequent subsets are introduced and proved first. Based on these two properties, the Weight judgment downward closure property-based FIM (WD-FIM) algorithm is proposed to narrow the searching space of the weighted frequent itemsets and improve the time efficiency. Moreover, the completeness and time efficiency of WD-FIM algorithm are analyzed theoretically. Finally, the performance of the proposed WD-FIM algorithm is verified on both synthetic and real-life data sets.