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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/17073

Title: Using K-Means Method and Spectral Clustering Technique in An Outfitter's Value Analysis
Authors: Chang, En-Chi;Huang, Shian-Chang;Wu, Hsin-Hung
Contributors: 企業管理學系
Keywords: K-means method;Spectral clustering technique;Cluster quality assessment;Marketing strategy;Customer value
Date: 2010-06
Issue Date: 2013-07-11T09:04:31Z
Publisher: Springer Science+Business Media B.V.
Abstract: 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.
Relation: Quality & Quantity, 44(4): 807-815
Appears in Collections:[企業管理學系] 期刊論文

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