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Concurrent sequential patterns mining and frequent partial orders modelling

  • Jing Lu
    ,
  • Malcolm Keech
    ,
  • Weiru Chen
    ,
  • Cuiqing Wang
  • Solent University
    ,
  • Shenyang Institute of Chemical Technology
Research Output: Contribution to journal Article Peer-review

Abstract

Structural relation patterns have been introduced to extend the search for complex patterns often hidden behind large sequences of data, with applications (e.g.) in the analysis of customer behaviour, bioinformatics and web mining. In the overall context of frequent itemset mining, the focus of attention in the structural relation patterns family has been on the mining of concurrent sequential patterns, where a companion approach to graph-based modelling can be illuminating. The crux of this paper sets out to establish the connection between concurrent sequential patterns and frequent partial orders, which are well known for discovering ordering information from sequence databases. It is shown that frequent partial orders can be derived from concurrent sequential patterns, under certain conditions, and worked examples highlight the relationship. Experiments with real and synthetic datasets contrast the results of the data mining and modelling involved.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 132

Journal (Volume, Issue Number)

International Journal of Business Intelligence and Data Mining (Volume 8, Issue 2)

Publication milestones

  • Published - 01/01/2013

Publication status

Published - 01/01/2013

ISSN

1743-8187

External Publication IDs

  • handle.net: 10547/333865
  • Scopus: 84889789664

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