Learning Multidimensional Sequential Patterns Using Hellinger Entropy Function 


Vol. 11,  No. 4, pp. 477-484, Aug.  2004
10.3745/KIPSTB.2004.11.4.477


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  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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[IEEE Style]

C. H. Lee, "Learning Multidimensional Sequential Patterns Using Hellinger Entropy Function," The KIPS Transactions:PartB , 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. The KIPS Transactions:PartB , 11, 4, (2004), 477-484. DOI: 10.3745/KIPSTB.2004.11.4.477.

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