Concurrent sequential patterns mining and frequent partial orders modelling
Affiliation
University of BedfordshireIssue Date
2013Subjects
sequential patterns post-processingstructural relation patterns
SRP
concurrent sequential patterns mining
frequent partial orders modelling
knowledge discovery
Metadata
Show full item recordAbstract
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.Citation
Lu, J., Keech, M., Chen, W., Wang, C. (2013) 'Concurrent sequential patterns mining and frequent partial orders modelling' International Journal of Business Intelligence and Data Mining 8 (2):132Publisher
Inderscience PublishersAdditional Links
http://www.inderscience.com/link.php?id=57751Type
ArticleLanguage
enISSN
1743-81871743-8195
ae974a485f413a2113503eed53cd6c53
10.1504/IJBIDM.2013.057751