Rough set-based attribute reduction and decision rule formulation for marketing data

Murchhana Tripathy, Anita Panda and Santilata Champati

International Journal of Data Analysis Techniques and Strategies, 2021, vol. 13, issue 3, 186-206

Abstract: Using the classical rough set theory concept, this study addresses the attribute reduction problem followed by decision rule formulation for marketing data that contains both inconsistence as well as repeated data. Based on the method followed in the work, we propose an algorithm which initially uses the concepts of core and reduct and then performs a cross checking of both by using the significance of the attributes to formulate more accurate and correct rules. For the borderline cases it is proposed to use the support and confidence of the rule to determine whether to select the rule or to exclude it. To show the working of the method discussed, we use the marketing data of 23 Indian cosmetic companies for the current study. Also we conduct a sensitivity analysis of the obtained results to gain insight about the profitability of the companies.

Keywords: discernibility matrix; core; reduct; significance of attributes; decision rules; marketing; sensitivity analysis. (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:13:y:2021:i:3:p:186-206

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