Efficient mining of closed repetitive gapped subsequences from a sequence database

Bolin Ding, David Lo, Jiawei Han, Siau Cheng Khoo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

There is a huge wealth of sequence data available, for example, customer purchase histories, program execution traces, DNA, and protein sequences. Analyzing this wealth of data to mine important knowledge is certainly a worthwhile goal. In this paper, as a step forward to analyzing patterns in sequences, we introduce the problem of mining closed repetitive gapped subsequences and propose efficient solutions. Given a database of sequences where each sequence is an ordered list of events, the pattern we would like to mine is called repetitive gapped subsequence, which is a subsequence (possibly with gaps between two successive events within it) of some sequences in the database. We introduce the concept of repetitive support to measure how frequently a pattern repeats in the database. Different from the sequential pattern mining problem, repetitive support captures not only repetitions of a pattern in different sequences but also the repetitions within a sequence. Given a userspecified support threshold min sup, we study finding the set of all patterns with repetitive support no less than min sup. To obtain a compact yet complete result set and improve the efficiency, we also study finding closed patterns. Efficient mining algorithms to find the complete set of desired patterns are proposed based on the idea of instance growth. Our performance study on various datasets shows the efficiency of our approach. A case study is also performed to show the utility of our approach.

Original languageEnglish (US)
Title of host publicationProceedings - 25th IEEE International Conference on Data Engineering, ICDE 2009
Pages1024-1035
Number of pages12
DOIs
StatePublished - 2009
Event25th IEEE International Conference on Data Engineering, ICDE 2009 - Shanghai, China
Duration: Mar 29 2009Apr 2 2009

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other25th IEEE International Conference on Data Engineering, ICDE 2009
CountryChina
CityShanghai
Period3/29/094/2/09

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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