Sequential pattern mining with multiple minimum supports: A tree based approach

Ya Han Hu, Fan Wu, Yi Chun Liao

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

3 Scopus citations

Abstract

Frequent pattern mining is an important data-mining method for determining correlations among items/itemsets. Since the frequencies for various items are always varied, specifying a single minimum support cannot exactly discover interesting patterns. To solve this problem, Liu et al. propose an apriori- based method to include the concept of multiple minimum supports (MMS in short) on association rule mining. It allows user to specify MMS to reflect the different natures of items. Since the mining of sequential pattern may face the same problem, we extend the traditional definition of sequential patterns to include the concept of MMS in this study. For efficiently discovering sequential patterns with MMS, we develop a data structure, named PLMS-tree, to store all necessary information from database. After that, a pattern growth method, named MSCP-growth, is developed to discover all sequential patterns with MMS from PLMS-tree.

Original languageEnglish
Title of host publication2nd International Conference on Software Engineering and Data Mining, SEDM 2010
Pages428-433
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

  • Multiple minimum supports
  • Pattern growth
  • Sequential pattern

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