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請使用永久網址來引用或連結此文件: http://ir.ncue.edu.tw/ir/handle/987654321/17073

題名: Using K-Means Method and Spectral Clustering Technique in An Outfitter's Value Analysis
作者: Chang, En-Chi;Huang, Shian-Chang;Wu, Hsin-Hung
貢獻者: 企業管理學系
關鍵詞: K-means method;Spectral clustering technique;Cluster quality assessment;Marketing strategy;Customer value
日期: 2010-06
上傳時間: 2013-07-11T09:04:31Z
出版者: Springer Science+Business Media B.V.
摘要: This study applies K-means method and spectral clustering technique in the customer data analysis of an outfitter in Taipei City, Taiwan. The data set contains transaction records of 551 customers from April 2004 to March 2006. The differences between the two clustering techniques mentioned here are significant. K-means method is more capable of dealing with linear separable input, while spectral clustering technique might have the advantage in non-linear separable input. Thus, it would be of interest to know which clustering technique performs better in a real-world case of evaluating customer value when the type of input space is unknown. By using cluster quality assessment, this study found that spectral clustering technique performs better than K-means method. To summarize the analysis, this study also suggests marketing strategies for each cluster based on the results generated by spectral clustering technique.
關聯: Quality & Quantity, 44(4): 807-815
顯示於類別:[企業管理學系] 期刊論文

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