Discriminating Subsequence Discovery for Sequence Clustering

Jianyong Wang, Yuzhou Zhang, Lizhu Zhou, George Karypis, Charu C. Aggarwal
SIAM International Conference on Data Mining (SDM), 2007
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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.
Research topics: Clustering | Data mining