CEMiner - An efficient algorithm for mining closed patterns from time interval-based data

Yi Cheng Chen*, Wen-Chih Peng, Suh Yin Lee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Scopus citations

Abstract

The mining of closed sequential patterns has attracted researchers for its capability of using compact results to preserve the same expressive power as conventional mining. However, existing studies only focus on time point-based data. Few research efforts have elaborated on discovering closed sequential patterns from time interval-based data, where each data persists for a period of time. Mining closed time intervalbased patterns, also called closed temporal patterns, is an arduous problem since the pairwise relationships between two interval-based events are intrinsically complex. In this paper, an efficient algorithm, CEMiner is developed to discover closed temporal patterns from interval-based data. Algorithm CEMiner employs some optimization techniques to effectively reduce the search space. The experimental results on both synthetic and real datasets indicate that CEMiner not only significantly outperforms the prior interval-based mining algorithms in terms of execution time but also possesses graceful scalability. The experiment conducted on real dataset shows the practicability of time interval-based closed pattern mining.

Original languageEnglish
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
Pages121-130
Number of pages10
DOIs
StatePublished - 1 Dec 2011
Event11th IEEE International Conference on Data Mining, ICDM 2011 - Vancouver, BC, Canada
Duration: 11 Dec 201114 Dec 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference11th IEEE International Conference on Data Mining, ICDM 2011
CountryCanada
CityVancouver, BC
Period11/12/1114/12/11

Keywords

  • Closed temporal pattern
  • Endpoint representation
  • Sequential pattern mining
  • Time interval-based data

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