Comparison of two fuzzy algorithms in geodemographic segmentation analysis: The fuzzy C-means and Gustafson-Kessel methods

Grekousis, G; Thomas, H

dc.contributor.author	Grekousis, G	en
dc.contributor.author	Thomas, H	en
dc.date.accessioned	2014-03-01T02:08:33Z
dc.date.available	2014-03-01T02:08:33Z
dc.date.issued	2012	en
dc.identifier.issn	01436228	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29658
dc.subject	Fuzzy C-means	en
dc.subject	Geodemographic segmentation	en
dc.subject	Gustfson-kessel algorithm	en
dc.subject.other	algorithm	en
dc.subject.other	census	en
dc.subject.other	cluster analysis	en
dc.subject.other	data set	en
dc.subject.other	experimental study	en
dc.subject.other	fuzzy mathematics	en
dc.subject.other	metropolitan area	en
dc.subject.other	segmentation	en
dc.subject.other	Athens [Attica]	en
dc.subject.other	Attica	en
dc.subject.other	Greece	en
dc.title	Comparison of two fuzzy algorithms in geodemographic segmentation analysis: The fuzzy C-means and Gustafson-Kessel methods	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.apgeog.2011.11.004	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.apgeog.2011.11.004	en
heal.publicationDate	2012	en
heal.abstract	Clustering techniques are frequently used to analyze census data and obtain meaningful large-scale groups. Geodemographic segmentation involves classifying small geographic areas e for example, block groups, census tracts, or neighborhoods - into relatively homogeneous segments. Most studies concerning geodemographic analysis and fuzzy logic employ the Fuzzy C-Means algorithm. In this paper, we compare two algorithms for fuzzy clustering in geodemographic analysis, and their structures, as well as their pros and cons, are analyzed. These are the Fuzzy C-Means algorithm and the GustafsoneKessel algorithm The main objective of this paper is to evaluate the performance of the Fuzzy C-Means and GustafsoneKessel algorithms in the clustering problem, under specific conditions. An experimental approach to this problem is adopted through the use of a real-world dataset describing 52 attributes of the 285 postal codes in the Athens metropolitan area. © 2011 Elsevier Ltd.	en
heal.journalName	Applied Geography	en
dc.identifier.doi	10.1016/j.apgeog.2011.11.004	en
dc.identifier.volume	34	en
dc.identifier.spage	125	en
dc.identifier.epage	136	en