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Weighted frequent itemset mining over uncertain databases

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Abstract

Frequent itemset mining (FIM) is a fundamental research topic, which consists of discovering useful and meaningful relationships between items in transaction databases. However, FIM suffers from two important limitations. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate, imprecise, or incomplete. To address these issues and mine more useful and meaningful knowledge, the problems of weighted and uncertain itemset mining have been respectively proposed, where a user may respectively assign weights to items to specify their relative importance, and specify existential probabilities to represent uncertainty in transactions. However, no work has addressed both of these issues at the same time. In this paper, we address this important research problem by designing a new type of patterns named high expected weighted itemset (HEWI) and the HEWI-Uapriori algorithm to efficiently discover HEWIs. The HEWI-Uapriori finds HEWIs using an Apriori-like two-phase approach. The algorithm introduces a property named high upper-bound expected weighted downward closure (HUBEWDC) to early prune the search space and unpromising itemsets. Substantial experiments on real-life and synthetic datasets are conducted to evaluate the performance of the proposed algorithm in terms of runtime, memory consumption, and number of patterns found. Results show that the proposed algorithm has excellent performance and scalability compared with traditional methods for weighted-itemset mining and uncertain itemset mining.

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Acknowledgments

This research was partially supported by the Tencent Project under grant CCF-TencentRAGR20140114, by the Shenzhen Peacock Project, China, under grant KQC201109020055A, by the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology under grant HIT.NSRIF.2014100, and by the Shenzhen Strategic Emerging Industries Program under grant ZDSY20120613125016389.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, 518055, People’s Republic of China

    Jerry Chun-Wei Lin & Wensheng Gan

  2. Department of Computer Science, University of Moncton, Moncton, Canada

    Philippe Fournier-Viger

  3. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, ROC, Taiwan

    Tzung-Pei Hong

  4. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, ROC, Taiwan

    Tzung-Pei Hong

  5. Department of Computer Science, National Chiao Tung University, Hsinchu, ROC, Taiwan

    Vincent S. Tseng

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  1. Jerry Chun-Wei Lin
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  2. Wensheng Gan
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  3. Philippe Fournier-Viger
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  4. Tzung-Pei Hong
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  5. Vincent S. Tseng
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Correspondence to Jerry Chun-Wei Lin.

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Lin, J.CW., Gan, W., Fournier-Viger, P. et al. Weighted frequent itemset mining over uncertain databases. Appl Intell 44, 232–250 (2016). https://doi.org/10.1007/s10489-015-0703-9

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