Title: Discovering exclusive patterns in frequent sequences

Authors: Weiru Chen, Jing Lu, Malcolm Keech

Addresses: Faculty of Computer Science and Technology, Shenyang Institute of Chemical Technology, Shenyang, 110142, China. ' School of Computing and Communications, 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

Abstract: This paper presents a new concept for pattern discovery in frequent sequences with potentially interesting applications. Based on data mining, the approach aims to discover exclusive sequential patterns (ESPs) by checking the relative exclusion of patterns across data sequences. ESP mining pursues the post-processing of sequential patterns and augments existing work on structural relations patterns mining. A three phase ESP mining method is proposed together with component algorithms, where a running worked example explains the process. Experiments are performed on real-world and synthetic datasets which showcase the results of ESP mining and demonstrate its effectiveness, illuminating the theories developed. An outline case study in workflow modelling gives some insight into future applicability.

Keywords: frequent sequences; data mining; sequential patterns; postprocessing; exclusive sequential patterns; ESP mining; workflow modelling; pattern discovery.

DOI: 10.1504/IJDMMM.2010.033536

International Journal of Data Mining, Modelling and Management, 2010 Vol.2 No.3, pp.252 - 267

Published online: 04 Jun 2010 *

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