Biclustering: overcoming data dimensionality problems in market segmentation

Dolnicar, Sara, Kaiser, Sebastian, Lazarevski, Katie and Leisch, Friedrich (2012) Biclustering: overcoming data dimensionality problems in market segmentation. Journal of Travel Research, 51 1: 41-49. doi:10.1177/0047287510394192

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Author Dolnicar, Sara
Kaiser, Sebastian
Lazarevski, Katie
Leisch, Friedrich
Title Biclustering: overcoming data dimensionality problems in market segmentation
Journal name Journal of Travel Research   Check publisher's open access policy
ISSN 0047-2875
1552-6763
ISBN 3-540-42486-5
Publication date 2012-01-01
Year available 2012
Sub-type Article (original research)
DOI 10.1177/0047287510394192
Open Access Status DOI
Volume 51
Issue 1
Start page 41
End page 49
Total pages 9
Place of publication Thousand Oaks, CA 91320 United States
Publisher Sage
Language eng
Abstract Data-driven market segmentation is a popular and widely used segmentation method in tourism. It aims to identify market segments among tourists who are similar to each other, thus allowing a targeted marketing mix to be developed. Typically data used to segment tourists are characterized by small numbers of respondents and large numbers of survey questions. Small samples and numerous questions cause serious methodological problems that have typically been addressed by using factorcluster analysis to reduce the dimensionality of data. Recently, factor-cluster analysis has been shown as an unacceptable solution to the problem of high data dimensionality in segmentation. In this article, the authors introduce biclustering, a novel approach to address the problem of high dimensionality in tourism segmentation studies. We discuss the circumstances in which biclustering should be used rather than parametric or nonparametric grouping techniques. An illustrative example of how biclustering is computed is also provided.
Keyword A posteriori market segmentation
Data-driven market segmentation
Cluster analysis
Factor-cluster analysis
Niche segments
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Non HERDC
UQ Business School Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 34 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 39 times in Scopus Article | Citations
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Created: Fri, 05 Apr 2013, 01:18:18 EST by Dr Kayleen Campbell on behalf of School of Tourism