Show simple item record

dc.contributor.authorChan, Kit Yan
dc.contributor.authorKwong, C.
dc.contributor.authorHu, B.
dc.identifier.citationChan, 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.

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.

dc.subjectfuzzy clustering
dc.subjectfuzzy compression
dc.subjectproduct design
dc.subjectdigital camera design
dc.subjectIdeal point
dc.subjectMarket segmentation
dc.titleMarket segmentation and ideal point identification for new product design using fuzzy data compression and fuzzy clustering methods
dc.typeJournal Article
dcterms.source.titleApplied Soft Computing

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

curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusOpen access

Files in this item


This item appears in the following Collection(s)

Show simple item record