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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Hybrid Temporal Mining for Finding Out Frequent Itemsets in Temporal Databases Using Clustering and Bit Vector Methods
verfasst von : M. Krishnamurthy, A. Kannan, R. Baskaran, G. Bhuvaneswari
Erschienen in: Information Intelligence, Systems, Technology and Management
Verlag: Springer Berlin Heidelberg
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Hybrid Temporal Pattern Mining was designed to address the problem of discovering frequent patterns of point and interval-based events or both and it is essential in many applications, including market analysis, decision support and business management. Such methodology cannot deal with Clustering, Bit Vector and Variable Threshold. In this paper, we propose a new algorithm called RHTPM (Revised Hybrid Temporal Pattern Mining) to find the frequent temporal pattern based on Clustering, Bit Vector and Variable Threshold. The experiments demonstrate that the proposed algorithm is capable of mining frequent hybrid temporal pattern for effective decision making and has been proved to be significantly good.