Multi-dimensional sequential pattern mining based on concept lattice

Authors:
Yang Jin;Wanli Zuo
Affiliations:
College of Computer Science & Technology, JiLin University, ChangChun, P.R. China;College of Computer Science & Technology, JiLin University, ChangChun, P.R. China
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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Abstract

Multi-dimensional sequential pattern mining attempts to find much more informative frequent patterns suitable for immediate use. In this paper, a novel data model called multi-dimensional concept lattice is proposed and, based on which, a new incremental multi-dimensional sequential pattern mining algorithm is developed. The proposed algorithm integrates sequential pattern mining and association pattern mining with a uniform data structure and makes the mining process more efficient. The performance of the proposed approach is evaluated on both synthetic and real-life financial date sets.