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

Authors: Murchhana Tripathy; Anita Panda; Santilata Champati

Addresses: Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar-751030, Odisha, India ' Department of Mathematics, Institute of Technical Education and Research, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar-751030, Odisha, India ' Department of Mathematics, Institute of Technical Education and Research, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar-751030, Odisha, India

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.

DOI: 10.1504/IJDATS.2021.118016

International Journal of Data Analysis Techniques and Strategies, 2021 Vol.13 No.3, pp.186 - 206

Received: 28 Dec 2018
Accepted: 13 Nov 2019
Published online: 08 Oct 2021 *

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