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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/17070

Title: A Case Study of Applying Data Mining Techniques in An Outfitter's Customer Value Analysis
Authors: Huang, Shian-Chang;Chang, En-Chi;Wu, Hsin-Hung
Contributors: 企業管理學系
Keywords: K-means method;Fuzzy c-means method;Bagged clustering algorithm;Value analysis;Cluster quality assessment
Date: 2009-04
Issue Date: 2013-07-11T09:04:29Z
Publisher: Elsevier Ltd.
Abstract: This study applies K-means method, fuzzy c-means clustering method and bagged clustering algorithm to the analysis of customer value for an outfitter in Taipei, Taiwan. These three techniques bear similar philosophy for data classification. Thus, it would be of interest to know which clustering technique performs best in a real world case of evaluating customer value. Using cluster quality assessment, this study concludes that bagged clustering algorithm outperforms the other two methods. To conclude the analyses, this study also suggests marketing strategies for each cluster based on the results generated by bagged clustering technique.
Relation: Expert Systems with Applications, 36(3): 5909-5915
Appears in Collections:[企業管理學系] 期刊論文

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