Hybrid approach for adaptive fuzzy grid partitioning and rule generation using rough set theory

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This research proposes a hybrid approach for adaptive fuzzy grid partitioning and rule generation using rough set theory to address the problem of customer segmentation based on purchasing behavior. The objective is to minimize the fuzziness of the partitioning while maximizing the accuracy and interpretability of the generated rules. The research utilizes a dataset consisting of customer transactions, including demographics, purchase details, and satisfaction ratings. The fuzzy grid partitioning process divides the customer space into grid cells, representing different segments. Fuzzy membership values are assigned to data points based on their association with each grid cell. Rough set theory is employed for attribute reduction, identifying the most relevant attributes for customer segmentation. Rule induction algorithms generate rules that capture the patterns and dependencies among customer attributes and their association with specific grid cells. The hybrid approach combines the advantages of adaptive fuzzy grid partitioning and rough set-based rule generation. The optimization process adjusts fuzzy membership values and refines the generated rules to improve accuracy and interpretability. A numerical example and a case study in the retail industry are presented to demonstrate the effectiveness of the proposed approach. Results show successful customer segmentation and generation of actionable rules for marketing strategies. The research contributes to the field of customer segmentation by providing a comprehensive methodology that integrates adaptive fuzzy grid partitioning and rule generation using rough set theory. The hybrid approach offers valuable insights into customer behavior, enabling targeted marketing campaigns, personalized recommendations, and enhanced customer satisfaction.