Learning Multidimensional Sequential Patterns Using Hellinger Entropy Function


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 4, pp. 477-484, Apr. 2004
10.3745/KIPSTB.2004.11.4.477, Full Text:

Abstract

The technique of sequential pattern mining means generating a set of inter-transaction patterns residing in time-dependent data. This paper proposes a new method for generating sequential patterns with the use of Hellinger measure. While the current methods are generating single dimensional sequential patterns within a single attribute, the proposed method is able to detect multi-dimensionalpatterns among different attributes. A number of heuristics, based on the characteristics of hellinger measure, are proposed to reduce the computational complexity of the sequential pattern systems. Some experimental results are presented.


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Cite this article
[IEEE Style]
C. H. Lee, "Learning Multidimensional Sequential Patterns Using Hellinger Entropy Function," KIPS Journal B (2001 ~ 2012) , vol. 11, no. 4, pp. 477-484, 2004. DOI: 10.3745/KIPSTB.2004.11.4.477.

[ACM Style]
Chang Hwan Lee. 2004. Learning Multidimensional Sequential Patterns Using Hellinger Entropy Function. KIPS Journal B (2001 ~ 2012) , 11, 4, (2004), 477-484. DOI: 10.3745/KIPSTB.2004.11.4.477.