Market segmentation and ideal point identification for new product design using fuzzy data compression and fuzzy clustering methods

Kit Yan Chan, Chun Kit Kwong, B. Q. Hu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)


In product design, various methodologies have been proposed for market segmentation, which group consumers with similar customer requirements into clusters. Central points on market segments are always used as ideal points of customer requirements for product design, which reflects particular competitive strategies to effectively reach all consumers' interests. However, existing methodologies ignore the fuzziness on consumers' customer requirements. In this paper, a new methodology is proposed to perform market segmentation based on consumers' customer requirements, which exist fuzziness. The methodology is an integration of a fuzzy compression technique for multi-dimension reduction and a fuzzy clustering technique. It first compresses the fuzzy data regarding customer requirements from high dimensions into two dimensions. After the fuzzy data is clustered into marketing segments, the centre points of market segments are used as ideal points for new product development. The effectiveness of the proposed methodology in market segmentation and identification of the ideal points for new product design is demonstrated using a case study of new digital camera design.
Original languageEnglish
Pages (from-to)1371-1378
Number of pages8
JournalApplied Soft Computing Journal
Issue number4
Publication statusPublished - 1 Apr 2012


  • Digital camera design
  • Fuzzy clustering
  • Fuzzy compression
  • Ideal point
  • Market segmentation
  • Product design

ASJC Scopus subject areas

  • Software

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