SeqIndex: Indexing sequences by sequential pattern analysis

Hong Cheng, Xifeng Yan, Jiawei Han

Research output: Contribution to conferencePaperpeer-review


In this paper, we study the issues related to the design and construction of high-performance sequence index structures in large sequence databases. To build effective indices, a novel method, called SeqIndex, is proposed, in which the selection of indices is based on the analysis of discriminative, frequent sequential patterns mined from large sequence databases. Such an analysis leads to the construction of compact and effective indexing structures. Furthermore, we eliminate the requirement of setting an optimal support threshold beforehand, which is difficult for users to provide in practice. The discriminative, frequent pattern based indexing method is proven very effective based on our performance study.

Original languageEnglish (US)
Number of pages5
StatePublished - 2005
Event5th SIAM International Conference on Data Mining, SDM 2005 - Newport Beach, CA, United States
Duration: Apr 21 2005Apr 23 2005


Other5th SIAM International Conference on Data Mining, SDM 2005
Country/TerritoryUnited States
CityNewport Beach, CA

ASJC Scopus subject areas

  • Software

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