Title: Concurrent sequential patterns mining and frequent partial orders modelling

Authors: Jing Lu; Malcolm Keech; Weiru Chen; Cuiqing Wang

Addresses: School of Technology, Southampton Solent University, East Park Terrace, Southampton, SO14 0YN, UK ' Faculty of Creative Arts, Technologies and Science, University of Bedfordshire, Park Square, Luton, LU1 3JU, UK ' Faculty of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, 110142, China ' Faculty of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, 110142, China

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.

Keywords: concurrent sequential patterns; sequential pattern post-processing; structural relation patterns; SRP; sequential pattern mining; frequent partial orders; modelling; knowledge discovery; data mining.

DOI: 10.1504/IJBIDM.2013.057751

International Journal of Business Intelligence and Data Mining, 2013 Vol.8 No.2, pp.132 - 154

Received: 15 Jul 2013
Accepted: 25 Jul 2013
Published online: 28 Jun 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article