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Mining High-Utility Irregular Itemsets

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High-Utility Pattern Mining

Part of the book series: Studies in Big Data ((SBD,volume 51))

Abstract

High-utility itemset mining (HUIM) currently plays an important role in a wide range of applications and data mining community. Several algorithms, methods and data structures have been proposed to improve efficiency of mining for such itemsets. Besides, HUIM is extended in several aspects including the regarding of “regularity or irregularity of occurrence” on high utility itemsets. This leads to the emerging of high-utility regular itemsets mining (HURIM) and high-utility irregular itemsets mining (HUIIM) which can help to observe occurrence behavior of high utility itemsets. Based on HURIM, the regularity threshold is applied to measure interestingness of itemsets and to prune search space. However, on HUIIM, the threshold cannot help to prune uninteresting itemsets causing this task consumes high computational cost. Thus, in this paper, we here present a single-pass algorithm, called HUIIM (High-Utility Irregular Itemset Miner), for efficiently mining high-utility irregular itemsets. The new-modified utility list structure (NUL) is applied for maintaining occurrence information simultaneously with utility value of an itemset and also for fast calculation of total utility of the itemset. Moreover, a new pruning technique is designed and applied to improve computational performance of HUIIM. Experimental studies were conducted on synthetic and real datasets to show efficiency of HUIIM (with and without the new pruning technique) in the terms of computational time and memory usage.

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Acknowledgements

This work was financially supported by the Research Grant of Burapha University through National Research Council of Thailand (Grant no. 15/2561).

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Authors and Affiliations

  1. Computational Innovation Laboratory, Burapha University, Chonburi, 20131, Thailand

    Supachai Laoviboon & Komate Amphawan

Authors
  1. Supachai Laoviboon
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  2. Komate Amphawan
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Correspondence to Komate Amphawan .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  2. Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  3. Université du Québec à Montréal, Montreal, QC, Canada

    Roger Nkambou

  4. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Bay Vo

  5. National Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

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Laoviboon, S., Amphawan, K. (2019). Mining High-Utility Irregular Itemsets. In: Fournier-Viger, P., Lin, JW., Nkambou, R., Vo, B., Tseng, V. (eds) High-Utility Pattern Mining. Studies in Big Data, vol 51. Springer, Cham. https://doi.org/10.1007/978-3-030-04921-8_7

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