Mining multi-level time-interval sequential patterns in sequence databases

Ya Han Hu, Fan Wu, Chieh I. Yang

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

3 Scopus citations

Abstract

Mining sequential patterns is an important issue in data mining and has many applications. An extended work of sequential pattern mining, called time-interval sequential pattern mining, is proposed to retrieve time-interval information between successive items. However, previous work only considers single-level time-interval in pattern extraction, which means sequential patterns with cross-level time-intervals are completely ignored. Therefore, this study first defines multi-level time-interval sequential patterns and then presents a novel algorithm, named MLTI-PrefixSpan, for discovering the complete set of multi-level time-interval sequential patterns. Experimental results show that the proposed algorithm is effective on the test dataset.

Original languageEnglish
Title of host publication2nd International Conference on Software Engineering and Data Mining, SEDM 2010
Pages416-421
Number of pages6
StatePublished - 2010
Event2nd International Conference on Software Engineering and Data Mining, SEDM 2010 - Chengdu, China
Duration: 23 Jun 201025 Jun 2010

Publication series

Name2nd International Conference on Software Engineering and Data Mining, SEDM 2010

Conference

Conference2nd International Conference on Software Engineering and Data Mining, SEDM 2010
Country/TerritoryChina
CityChengdu
Period23/06/1025/06/10

Keywords

  • Data mining
  • Sequential patterns
  • Time-interval

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