Mining Frequent Ordered Patterns

Zhi-Hong Deng, Cong-Rui Ji, Ming Zhang, Shi-Wei Tang

Abstract: Mining frequent patterns has been studied popularly in data mining research. All of previous studies assume that items in a pattern are unordered. However, the order existing between items must be considered in some applications. In this paper, we first give the formal model of ordered patterns and discuss the problem of mining frequent ordered patterns. Base on our analyses, we present two efficient algorithms for mining frequent ordered patterns. We also present results of applying these algorithms to a synthetic data set, which show the effectiveness of our algorithms.