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Fast and memory efficient mining of high-utility itemsets from data streams: with and without negative item profits

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Abstract

Mining utility itemsets from data steams is one of the most interesting research issues in data mining and knowledge discovery. In this paper, two efficient sliding window-based algorithms, MHUI-BIT (Mining High-Utility Itemsets based on BITvector) and MHUI-TID (Mining High-Utility Itemsets based on TIDlist), are proposed for mining high-utility itemsets from data streams. Based on the sliding window-based framework of the proposed approaches, two effective representations of item information, Bitvector and TIDlist, and a lexicographical tree-based summary data structure, LexTree-2HTU, are developed to improve the efficiency of discovering high-utility itemsets with positive profits from data streams. Experimental results show that the proposed algorithms outperform than the existing approaches for discovering high-utility itemsets from data streams over sliding windows. Beside, we also propose the adapted approaches of algorithms MHUI-BIT and MHUI-TID in order to handle the case when we are interested in mining utility itemsets with negative item profits. Experiments show that the variants of algorithms MHUI-BIT and MHUI-TID are efficient approaches for mining high-utility itemsets with negative item profits over stream transaction-sensitive sliding windows.

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Authors and Affiliations

  1. Department of Information Management, Kainan University, Taoyuan, Taiwan

    Hua-Fu Li

  2. Department of Computer Science, National Chiao-Tung University, Hsinchu, Taiwan

    Hsin-Yun Huang & Suh-Yin Lee

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  1. Hua-Fu Li
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  2. Hsin-Yun Huang
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  3. Suh-Yin Lee
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Correspondence to Hua-Fu Li.

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Li, HF., Huang, HY. & Lee, SY. Fast and memory efficient mining of high-utility itemsets from data streams: with and without negative item profits. Knowl Inf Syst 28, 495–522 (2011). https://doi.org/10.1007/s10115-010-0330-z

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  • DOI: https://doi.org/10.1007/s10115-010-0330-z

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