A case study of applying data mining techniques in an outfitter's customer value analysis

Research output: Contribution to journalArticle

42 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)5909-5915
Number of pages7
JournalExpert Systems with Applications
Volume36
Issue number3 PART 2
DOIs
Publication statusPublished - 2009 Jan 1

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Value engineering
Clustering algorithms
Data mining
Marketing

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

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A case study of applying data mining techniques in an outfitter's customer value analysis. / Huang, Shian-Chang; Chang, En Chi; Wu, Hsin-Hung.

In: Expert Systems with Applications, Vol. 36, No. 3 PART 2, 01.01.2009, p. 5909-5915.

Research output: Contribution to journalArticle

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