Online mining of temporal maximal utility itemsets from data streams
Proceedings of the 2010 ACM Symposium on Applied Computing
Interactive mining of high utility patterns over data streams
Expert Systems with Applications: An International Journal
Mining top-K high utility itemsets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Expert Systems with Applications: An International Journal
Mining high utility episodes in complex event sequences
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
UT-Tree: Efficient mining of high utility itemsets from data streams
Intelligent Data Analysis
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Efficient mining of high utility itemsets has become one of the most interesting data mining tasks with broad applications. In this paper, we proposed two efficient one-pass algorithms, MHUI-BIT and MHUI-TID, for mining high utility itemsets from data streams within a transaction-sensitive sliding window. Two effective representations of item information and an extended lexicographical tree-based summary data structure are developed to improve the efficiency of mining high utility itemsets. Experimental results show that the proposed algorithms outperform than the existing algorithms for mining high utility itemsets from data streams.