An Improving Approach for Top-Rank-k Frequent Pattern Mining

Abstract

Mining frequent patterns (FPs) received a lot of research interest which is a fundamental problem in the field of data mining. It has been studied on many database types with various applications in intelligent systems. However, the traditional methods using a given threshold to mine FPsterns suffer from a crucial drawback, that is, the number of resulting patterns is uncontrollable, sometimes very large. Mining top-rank-[Formula: see text] FPs is one of the current research directions to solve the aforementioned disadvantage of mining FPs. Many methods have been proposed to mine effectively top-rank-[Formula: see text] frequent patterns. However, these methods also have many limitations regarding memory consumption and suboptimal mining time. Therefore, this study proposes a new method based on [Formula: see text]-list structure using the Priority Queue, the Hash Table structure, and some fast pruning strategies for efficient mining top-rank-[Formula: see text] FPs from binary transaction databases. The experimental results on many datasets demonstrate that the proposed method is more effective than the previous methods in the terms of runtime and memory usage.