A block-based approach for frequent itemset mining over data streams | IEEE Conference Publication | IEEE Xplore

A block-based approach for frequent itemset mining over data streams


Abstract:

Sliding window is a widely used model for data stream processing and mining. Frequent itemset mining over sliding window is a challenging problem due to limited processin...Show More

Abstract:

Sliding window is a widely used model for data stream processing and mining. Frequent itemset mining over sliding window is a challenging problem due to limited processing resources. In this study, an efficient representation of sliding window is proposed. In this representation, a blocked bit sequence technique is used to enhance both sliding and mining time. Experimental evaluations show that our algorithm outperforms a recently proposed algorithm.
Date of Conference: 26-28 July 2011
Date Added to IEEE Xplore: 15 September 2011
ISBN Information:
Conference Location: Shanghai, China

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