Apply robust segmentation to the service industry using kernel induced fuzzy clustering techniques

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

To understand customers' characteristics and their desire is critical for modern CRM (customer relationship management). The easiest way for a company to achieve this goal is to target their customers and then to serve them through providing a variety of personalized and satisfactory goods or service. In order to put the right products or services and allocate resources to specific targeted groups, many CRM researchers and/or practitioners attempt to provide a variety of ways for effective customer segmentation. Unfortunately, most existing approaches are vulnerable to outliers in practice and hence segmentation results may be unsatisfactory or seriously biased. In this study, a hybrid approach that incorporates kernel induced fuzzy clustering techniques is proposed to overcome the above-mentioned difficulties. Two real datasets, including the WINE and the RFM, are used to validate the proposed approach. Experimental results show that the proposed approach cannot only fulfill robust classification, but also achieve robust segmentation when applied to the noisy dataset.

Original languageEnglish
Pages (from-to)8395-8400
Number of pages6
JournalExpert Systems with Applications
Volume37
Issue number12
DOIs
StatePublished - 1 Jan 2010

Keywords

  • Kernel induced fuzzy clustering
  • Robust classification
  • Robust segmentation

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