Post sequential patterns mining: a new method for discovering structural patterns

Authors:
Jing Lu;Osei Adjei;Weiru Chen;Jun Liu
Affiliations:
School of Computer Science and Technology, Shenyang Institute of Chemical Technology, Shenyang, China;Department of Computing and Information Systems, University of Luton, Park Sq. Luton, UK;School of Computer Science and Technology, Shenyang Institute of Chemical Technology, Shenyang, China;School of Computer Science and Technology, Shenyang Institute of Chemical Technology, Shenyang, China
Venue:
Intelligent information processing II
Year:
2004

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Abstract

In this paper we present a novel data mining technique, known as Post Sequential Patterns Mining, which can be used to discover Structural Patterns. A Structural Pattern is a new pattern, which is composed of sequential patterns, branch patterns or iterative patterns. Sequential patterns mining plays an essential role in many areas and substantial research has been conducted on their analysis and applications. In our previous work [12], we used a simple but efficient Sequential Patterns Graph (SPG) to model the sequential patterns. The task to discover hidden Structural Pattern is based on our previous work and sequential patterns mining, conveniently named Post Sequential Patterns Mining. In this paper, in addition to stating this new mining problem, we define patterns such as branch pattern, iterative pattern, structural pattern, and concentrate on finding concurrent branch pattern. Concurrent branch pattern is thus one of the main forms of structural pattern and will play an important role in event-based data modelling.