Mining Top-Rank-K Frequent Patterns

Abstract

There have been many studies on efficient discovery of frequent patterns in large databases. The usual framework is to use a minimal support threshold to obtain all frequent patterns. However, it is nontrivial for users to choose a suitable minimal support threshold. In this paper, a new mining task called mining top-rank-k frequent patterns, where k is the biggest rank value of all frequent patterns to be mined, has been proposed. After deep analyzing the properties of top-rank-k frequent patterns, we propose an efficient algorithm called FAE to mining top-rank-k frequent patterns. FAE is the abbreviation of "Filtering and Extending ". During the mining process of FAE, the undesired patterns are filtered and useful patterns are selected to generate other longer potential frequent patterns. This strategy greatly reduces the search space. We also present results of applying these algorithms to a synthetic data set, which show the effectiveness of our algorithms.