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dc.contributor.authorHuang, Jih-Jengen_US
dc.contributor.authorTzeng, Gwo-Hshiungen_US
dc.contributor.authorong, Chorng-Shy Ongen_US
dc.date.accessioned2014-12-08T15:14:47Z-
dc.date.available2014-12-08T15:14:47Z-
dc.date.issued2007-02-01en_US
dc.identifier.issn0957-4174en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2005.11.028en_US
dc.identifier.urihttp://hdl.handle.net/11536/11172-
dc.description.abstractMarketing segmentation is widely used for targeting a smaller market and is useful for decision makers to reach all customers effectively with one basic marketing mix. Although several clustering algorithms have been proposed to deal with marketing segmentation problems, a soundly method seems to be limited. In this paper, support vector clustering (SVC) is used for marketing segmentation. A case study of a drink company is used to demonstrate the proposed method and compared with the k-means and the self-organizing feature map (SOFM) methods. On the basis of the numerical results, we can conclude that SVC outperforms the other methods in marketing segmentation. (C) 2005 Published by Elsevier Ltd.en_US
dc.language.isoen_USen_US
dc.subjectmarketing segmentationen_US
dc.subjectclustering algorithmsen_US
dc.subjectsupport vector clustering (SVC)en_US
dc.subjectk-meansen_US
dc.subjectself-organizing feature map (SOFM)en_US
dc.titleMarketing segmentation using support vector clusteringen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.eswa.2005.11.028en_US
dc.identifier.journalEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.citation.volume32en_US
dc.citation.issue2en_US
dc.citation.spage313en_US
dc.citation.epage317en_US
dc.contributor.department科技管理研究所zh_TW
dc.contributor.departmentInstitute of Management of Technologyen_US
dc.identifier.wosnumberWOS:000242979100005-
dc.citation.woscount25-
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