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

    172019_172019.pdf (135.7Kb)
    Access Status
    Open access
    Authors
    Chan, Kit Yan
    Kwong, C.
    Hu, B.
    Date
    2011
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Chan, Kit Yang and Kwong, C.K. and Hu, B.Q. 2012. Market segmentation and ideal point identification for new product design using fuzzy data compression and fuzzy clustering methods. Applied Soft Computing. 12 (4): pp. 1371-1378.
    Source Title
    Applied Soft Computing
    DOI
    10.1016/j.asoc.2011.11.026
    ISSN
    15684946
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    Remarks

    NOTICE: this is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, Vol.12, no.4 (April 2012). DOI: 10.1016/j.asoc.2011.11.026

    URI
    http://hdl.handle.net/20.500.11937/16373
    Collection
    • Curtin Research Publications
    Abstract

    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.

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