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High average-utility itemsets mining: a survey

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Abstract

HUIM (High utility itemsets mining) is a sub-division of data mining dealing with the task to obtain promising patterns in the quantitative datasets. A variant of HUIM is to discover the HAUIM (High average-utility itemsets mining) where average-utility measure is used to obtain the utility of itemsets. HAUIM is the refined version of FIM (Frequent itemset mining) problem and has various applications in the field of market basket analysis, bio-informatics, text mining, network traffic analysis, product recommendation and e-learning among others. In this paper, we provide a comprehensive survey of the state-of-the-art methods of HAUIM to mine the HAUIs (High average-utility itemsets) from the static and dynamic datasets since the induction of the HAUIM problem. We discuss the pros and cons of each category of mining approaches in detail. The taxonomy of HAUIM is presented according to the mining approaches. Finally,various extensions, future directions and research opportunities of HAUIM algorithms are discussed.

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Notes

  1. Any superset of a non-frequent itemset is also non-frequent.

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Authors and Affiliations

  1. Department of Computer Science, University of Delhi, Delhi, India

    Kuldeep Singh

  2. Faculty of Engineering and Technology, Botho University, Gaborone, Botswana

    Rajiv Kumar

  3. Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India

    Bhaskar Biswas

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  1. Kuldeep Singh
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Singh, K., Kumar, R. & Biswas, B. High average-utility itemsets mining: a survey. Appl Intell 52, 3901–3938 (2022). https://doi.org/10.1007/s10489-021-02611-z

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