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Application of Association Rule Mining in a Clothing Retail Store

In: Applied Advanced Analytics

Author

Listed:
  • Akshay Jain

    (SVKM’s Narsee Monjee Institute of Management Studies (NMIMS))

  • Shrey Jain

    (SVKM’s Narsee Monjee Institute of Management Studies (NMIMS))

  • Nitin Merh

    (Jaipuria Institute of Management Indore)

Abstract

In this paper, an attempt has been made to understand the buying pattern of customer with the help of market basket analysis, which is an important tool in modern retailing industry. Retailing is defined as the timely delivery of goods and services demanded by end customers at prices that are competitive and affordable. Through association rule in data mining, we have tried to understand consumer behavior, brand importance, seasonality effect, buying pattern, product basket from the data. Data mining is the practice of analyzing database to gather and generate new information. Association rule mining is a process of finding rules that govern relation between sets of items. Market basket analysis is a modeling technique based upon the theory that explains the buying relation between certain groups of items. The transactional data is collected from the retail clothing store “Try Us” located in Indore, Madhya Pradesh, from the period November 26, 2017, to September 19, 2018, with the help of Point of Sale (POS) and bar code scanner. “Try Us” is a small retail store with multiple brands and is planning to upgrade store to a multilevel store. Data mining would benefit the overall store performance. Frontline Solver® Analytic Solver Data Mining (XLMiner) is used for simulations.

Suggested Citation

  • Akshay Jain & Shrey Jain & Nitin Merh, 2021. "Application of Association Rule Mining in a Clothing Retail Store," Springer Proceedings in Business and Economics, in: Arnab Kumar Laha (ed.), Applied Advanced Analytics, pages 103-114, Springer.
    • Handle: RePEc:spr:prbchp:978-981-33-6656-5_9
    DOI: 10.1007/978-981-33-6656-5_9
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