CONTOUR: An efficient algorithm for discovering discriminating subsequences

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

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


In recent years we have witnessed several applications of frequent sequence mining, such as feature selection for protein sequence classification and mining block correlations in storage systems. In typical applications such as clustering, it is not the complete set but only a subset of discriminating frequent subsequences which is of interest. One approach to discovering the subset of useful frequent subsequences is to apply any existing frequent sequence mining algorithm to find the complete set of frequent subsequences. Then, a subset of interesting subsequences can be further identified. Unfortunately, it is very time consuming to mine the complete set of frequent subsequences for large sequence databases. In this paper, we propose a new algorithm, CONTOUR, which efficiently mines a subset of high-quality subsequences directly in order to cluster the input sequences. We mainly focus on how to design some effective search space pruning methods to accelerate the mining process and discuss how to construct an accurate clustering algorithm based on the result of CONTOUR. We conducted an extensive performance study to evaluate the efficiency and scalability of CONTOUR, and the accuracy of the frequent subsequence-based clustering algorithm.

Original languageEnglish (US)
Pages (from-to)1-29
Number of pages29
JournalData Mining and Knowledge Discovery
Issue number1
StatePublished - Feb 2009

Bibliographical note

Funding Information:
Acknowledgements Jianyong Wang was supported in part by National Basic Research Program of China under Grant No. 2006CB303103, Program for Selected Talents (i.e., “Gu Gan Ren Cai") in Tsinghua University, Program for New Century Excellent Talents in University under Grant No. NCET-07-0491, and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China. George Karypis was supported by NSF EIA-9986042, ACI-0133464, IIS-0431135, NIH RLM008713A, NIH T32GM008347, the Digital Technology Center, University of Minnesota and the Minnesota Supercomputing Institute. This paper is a major-value added version of a conference paper that appeared in the 2007 SIAM International Conference on Data Mining (SIAM SDM’07).


  • Clustering
  • Discriminating subsequence
  • Sequence mining
  • Summarization subsequence


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