Analysing Destination Image Data Using Rough Clustering

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Title Analysing Destination Image Data Using Rough Clustering
Author Voges, Kevin E.; Pope, Nigel Kenneth
Publication Title ANZMAC 2009
Editor Dr Dewi Tojib
Year Published 2009
Place of publication Canning Bridge, Western Australia
Publisher Promaco Conventions (for ANZMAC)
Abstract Cluster analysis is a fundamental data analysis technique, but many clustering methods have limitations, such as requiring initial starting points and requiring that the number of clusters be specified in advance. This paper describes an evolutionary algorithm based rough clustering algorithm, which is able to overcome these limitations. Rough clusters use sub-clusters called lower and upper approximations. The lower approximation of a rough cluster contains objects that only belong to that cluster, while the upper approximation contains objects that can belong to more than one cluster. The approach therefore allows for multiple cluster membership for data objects. This rough clustering algorithm was tested on a large data set of perceptions of city destination image attributes, and some preliminary results are presented.
Peer Reviewed Yes
Published Yes
Publisher URI
Copyright Statement Copyright 2009 ANZMAC. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the conference website for access to the definitive, published version.
ISBN 1863081607
Conference name Australian and New Zealand Marketing Academy (ANZMAC) Conference 2009
Location Melbourne, Australia
Date From 2009-11-30
Date To 2009-12-02
Date Accessioned 2010-03-01
Language en_AU
Faculty Griffith Business School
Subject Marketing Research Methodology
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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