Concurrent sequential patterns mining and frequent partial orders modelling

Jing Lu

Research output: Contribution to journalArticlepeer-review


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. Keywords: sequential patterns post-processing, structural relation patterns, concurrent sequential patterns mining, frequent partial orders modelling, knowledge discovery, business intelligence.
Original languageEnglish
Pages (from-to)132-154
Number of pages23
JournalInternational Journal of Business Intelligence and Data Mining
Issue number2
Publication statusPublished - 1 Dec 2013
Externally publishedYes


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