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The research aimed to develop a methodological approach for clustering of customers based on their characteristics in order to define membership cards based on mathematical optimization in a hypermarket.Data mining as a technique is used to find interesting and valuable knowledge from huge amount of stored data within databases. We employed hierarchical and fuzzy clustering method in data selection preprocessing step for customer segmentation. Further, a methodological approach for clustering of customers based on their characteristics in order to define membership cards based on mathematical…mehr

Produktbeschreibung
The research aimed to develop a methodological approach for clustering of customers based on their characteristics in order to define membership cards based on mathematical optimization in a hypermarket.Data mining as a technique is used to find interesting and valuable knowledge from huge amount of stored data within databases. We employed hierarchical and fuzzy clustering method in data selection preprocessing step for customer segmentation. Further, a methodological approach for clustering of customers based on their characteristics in order to define membership cards based on mathematical optimization is devised in this research. This study provides a basis for generating customer membership cards in a hypermarket by way of data mining techniques. Fuzzy clustering method helps to cluster similar customer information into groups. The resulting clusters are then used for optimization model in order to generate membership cards.
Autorenporträt
The author received Master's Degree in Computer Science from Blekinge Institute of Technology (BTH),SWEDEN. He is currently dedicated to RDBMS, SQL SERVER work as a Data Analyst.