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A Review of High Utility Itemset Mining for Transactional Database

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Pattern Recognition and Data Analysis with Applications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 888))

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Abstract

High utility itemset mining (HUIM) is an expansion of frequent itemset mining (FIM). Both of them are techniques to find interesting patterns from the database. The interesting patterns found by FIM are based on frequently appeared items. This approach is not that efficient to identify the desired patterns, as it considers only existence or nonexistence of items in database and ignores utility. However, the patterns are more meaningful for the user if the utility is considered. The utility can be quantity, profit, cost, risk, or other factors based on user interest. HUIM is another approach to find interesting patterns by considering utility of items along with the frequency. It uses minimum utility threshold to determine if an itemset is high utility itemset (HUI) or not. There are several challenges to implement utility from traditional pattern mining to HUIM. Lately, there are many research contributions that proposed different algorithms to solve these issues. This review work explores various HUIM techniques with detailed analysis of different strategies like apriori, tree based, utility lists based, and hybrid. These strategies are used to implement various HUIM techniques in order to achieve the effectiveness in pattern mining. The observations and analytical findings based on this detailed review done with respect to various parameters can be recommended and used for further research in the pattern mining.

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Authors and Affiliations

  1. St. Francis Institute of Technology, Mumbai, India

    Eduardus Hardika Sandy Atmaja & Kavita Sonawane

  2. Sanata Dharma University, Yogyakarta, Indonesia

    Eduardus Hardika Sandy Atmaja

Authors
  1. Eduardus Hardika Sandy Atmaja
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  2. Kavita Sonawane
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Correspondence to Eduardus Hardika Sandy Atmaja .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Arunachal Pradesh, Jote, Arunachal Pradesh, India

    Deepak Gupta

  2. Department of Computer Science and Engineering, National Institute of Technology Arunachal Pradesh, Jote, Arunachal Pradesh, India

    Rajat Subhra Goswami

  3. Department of Computer Science and Engineering, National Institute of Technology Arunachal Pradesh, Jote, Arunachal Pradesh, India

    Subhasish Banerjee

  4. Department of Mathematics, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    M. Tanveer

  5. Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Ram Bilas Pachori

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Atmaja, E.H.S., Sonawane, K. (2022). A Review of High Utility Itemset Mining for Transactional Database. In: Gupta, D., Goswami, R.S., Banerjee, S., Tanveer, M., Pachori, R.B. (eds) Pattern Recognition and Data Analysis with Applications. Lecture Notes in Electrical Engineering, vol 888. Springer, Singapore. https://doi.org/10.1007/978-981-19-1520-8_2

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  • DOI: https://doi.org/10.1007/978-981-19-1520-8_2

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