Mining non-derivable frequent itemsets over data stream

Authors:
Haifeng Li;Hong Chen
Affiliations:
School of Information, Renmin University of China, Beijing 100872, China;School of Information, Renmin University of China, Beijing 100872, China and Key Laboratory of Data Engineering and Knowledge Engineering, MOE, Beijing 100872, China
Venue:
Data & Knowledge Engineering
Year:
2009

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Abstract

Non-derivable frequent itemsets are one of several condensed representations of frequent itemsets, which store all of the information contained in frequent itemsets using less space, thus being more suitable for stream mining. This paper considers a problem that to the best of our knowledge has not been addressed, namely, how to mine non-derivable frequent itemsets in an incremental fashion. We design a compact data structure named NDFIT to efficiently maintain a dynamically selected set of itemsets. In NDFIT, the nodes are divided into four categories to reduce the redundant computational cost based on their properties. Consequently, an optimized algorithm named NDFIoDS is proposed to generate non-derivable frequent itemsets over stream sliding window. Our experimental results show that this method is effective and more efficient than previous approaches.