Discriminating Subsequence Discovery for Sequence Clustering

Jianyong Wang, Yuzhou Zhang, Lizhu Zhou, George Karypis, Charu C. Aggarwal

Abstract: In this paper, we explore the discriminating subsequence-based clustering problem. First, several effective optimization techniques are proposed to accelerate the sequence mining process and a new algorithm, CONTOUR, is developed to efficiently and directly mine a subset of discriminating frequent subsequences which can be used to cluster the input sequences. Second, an accurate hierarchical clustering algorithm, SSC, is constructed based on the result of CONTOUR. The performance study evaluates the efficiency and scalability of CONTOUR, and the clustering quality of SSC.