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http://dx.doi.org/10.3745/KIPSTB.2004.11B.4.477

Learning Multidimensional Sequential Patterns Using Hellinger Entropy Function  

Lee, Chang-Hwan (동국대학교 정보통신학과)
Publication Information
The KIPS Transactions:PartB / v.11B, no.4, 2004 , pp. 477-484 More about this Journal
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-dimensional patterns 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.
Keywords
Data Mining; Sequential Pattern; Machine Learning;
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