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High Average Utility Itemset Mining: A Survey

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Proceedings of International Conference on Computational Intelligence and Data Engineering

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 56))

Abstract

The past two decades has seen a great amount of research being done in the area of “Data Mining”. “Frequent Itemset Mining” is one application of “Data Mining” used to mine “Frequent Patterns” from databases. Itemsets that occur frequently only extract patterns that are frequent. This type of mining has its disadvantage. Items that are frequent may or may not be profitable to an organization. For this type of disadvantage to be overcome “High Utility Itemset Mining” had been introduced. It is used to mine profitable patterns from databases. This can help organizations to market profitable patterns. “High Utility Itemset Mining” also has its own disadvantage, because while mining itemsets, the lengths of the itemsets are not taken into consideration. The bigger the length of the itemset the more the profit. This does not give a real representation of the value or profit of the itemset. To get over this problem “High Average Utility Itemset Mining” had been introduced.

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Authors and Affiliations

  1. Sardar Vallabhbhai National Institute of Technology, Surat, India

    Mathe John Kenny Kumar & Dipti Rana

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  1. Mathe John Kenny Kumar
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  2. Dipti Rana
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Correspondence to Mathe John Kenny Kumar .

Editor information

Editors and Affiliations

  1. University of Calcutta, Kolkata, India

    Nabendu Chaki

  2. West Pomeranian University of Technology, Szczecin, Poland

    Jerzy Pejas

  3. VIT-AP University, Amaravati, India

    Nagaraju Devarakonda

  4. Vasavi College of Engineering, Hyderabad, India

    Ram Mohan Rao Kovvur

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Kenny Kumar, M.J., Rana, D. (2021). High Average Utility Itemset Mining: A Survey. In: Chaki, N., Pejas, J., Devarakonda, N., Rao Kovvur, R.M. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 56. Springer, Singapore. https://doi.org/10.1007/978-981-15-8767-2_30

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