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An improved scheme for determining top-revenue itemsets for placement in retail businesses

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Abstract

Utility mining has been emerging as an important area in data mining. While existing works on utility mining for retail businesses have primarily focused on the problem of finding high-utility itemsets from transactional databases, they implicitly assume that each item occupies only one slot. Here, the slot size of a given item is the number of (integer) slots occupied by that item on the retail store shelves. However, in many real-world scenarios, the number of slots consumed by different items typically varies. Hence, this paper considers that a given item may physically occupy any fixed (integer) number of slots. Thus, we address the problem of efficiently determining the top-utility itemsets when a given number of slots is specified as input. The key contributions of our work are three fold. First, we present an efficient framework to determine the top-utility itemsets for different user-specified number of slots that need to be filled. Second, we propose a novel flexible and efficient index, designated as Slot Type Utility (STU) index, for facilitating quick retrieval of the top-utility itemsets for a given number of slots. Third, we conducted an extensive performance evaluation using both real and synthetic datasets to demonstrate the overall effectiveness of the STU index in quickly retrieving the top-utility itemsets by considering a placement scheme in terms of execution time and utility (net revenue) as compared to recent existing schemes.

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Authors and Affiliations

  1. Shiv Nadar University, Greater Noida, India

    Parul Chaudhary

  2. Ashoka University, Sonipat, India

    Anirban Mondal

  3. International Institute of Information Technology, Hyderabad, India

    Polepalli Krishna Reddy

Authors
  1. Parul Chaudhary
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  2. Anirban Mondal
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  3. Polepalli Krishna Reddy
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Correspondence to Parul Chaudhary.

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Chaudhary, P., Mondal, A. & Reddy, P.K. An improved scheme for determining top-revenue itemsets for placement in retail businesses. Int J Data Sci Anal 10, 359–375 (2020). https://doi.org/10.1007/s41060-020-00221-5

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