DATA MINING USING FUZZY METHOD FOR CUSTOMER RELATIONSHIP MANAGEMENT IN RETAIL INDUSTRY

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Yohana Nugraheni

Abstract

A problem that appears in a retail industry with a great quantity of customers is how to identify potential customers. A retail industry could identify their best customer through customer segmentation by applying data miningand customer relationship managementconcept. This paper presents data mining process from customer's data in retail company by combining fuzzy RFM model with fuzzy c-meansand fuzzy subtractive algorithm. The dataconsisted of 3.000.000 rows of transaction data from 2006 to 2010. The data transferred to 499 RFM data for each time period selected. Experiments tried two to six clusters by changing the value of cluster number (FCM) and radii(fuzzy subtractive). The clustering result will then be classified to determine customer segmentation using fuzzy RFM models. The modified partition coefficient and partition entropy indexes used to evaluate the performance of both clustering algorithm.The results indicate that FCM has a higher validity rate than fuzzy subtractive. Fuzzy RFM segmentationindicates that fuzzy subtractive can not form a cluster that are categorized as potential customers, therefore FCM is more appropriate for customer segmentation in retail industry.

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How to Cite
NUGRAHENI, Yohana. DATA MINING USING FUZZY METHOD FOR CUSTOMER RELATIONSHIP MANAGEMENT IN RETAIL INDUSTRY. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], nov. 2015. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/16717>. Date accessed: 29 nov. 2020.
Keywords
fuzzy RFM model,fuzzy c-means, fuzzy subtractive, modified partition coefficient, partition entropy
Section
Articles