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High Utility Itemset Mining and Inventory Management: Theory and Use Cases

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Intelligent Data Engineering and Analytics (FICTA 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 371))

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Abstract

High utility itemset mining is a recent trend of finding not the most frequent items sold in the store, but finding the items sold of high utility to the store in terms of price and quantity. The knowledge gained from high utility itemset mining can be utilized in multiple ways for managing the inventory of a store. This paper envisions possible use cases of high utility itemset mining to inventory management. The use cases of this paper are based on a few synthetic examples and a real-world dataset in the retail domain. The motivation of the paper is to broaden the horizon by suggesting a few possible uses of high utility itemset mining in inventory management.

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Authors and Affiliations

  1. Administrative Staff College of India, Raj Bhavan Road, Khairtabad, Hyderabad, Telangana, 500082, India

    Gutha Jaya Krishna

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  1. Gutha Jaya Krishna
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Correspondence to Gutha Jaya Krishna .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology (UNSIET), Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Fiona Carroll

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    João Manuel R. S. Tavares

  4. Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Sandeep Singh Sengar

  5. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Peter Peer

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Krishna, G.J. (2023). High Utility Itemset Mining and Inventory Management: Theory and Use Cases. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_6

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