dc.creator Grekousis, G. en dc.creator Thomas, H. en dc.date.accessioned 2015-11-23T10:29:12Z dc.date.available 2015-11-23T10:29:12Z dc.date.issued 2012 dc.identifier 10.1016/j.apgeog.2011.11.004 dc.identifier.issn 1436228 dc.identifier.uri http://hdl.handle.net/11615/28213 dc.description.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 dc.source.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-84155179103&partnerID=40&md5=f947e6c19daad8c677fb8941d1b865f9 dc.subject Fuzzy C-means en dc.subject Geodemographic segmentation en dc.subject Gustfson-kessel algorithm en dc.subject algorithm en dc.subject census en dc.subject cluster analysis en dc.subject data set en dc.subject experimental study en dc.subject fuzzy mathematics en dc.subject metropolitan area en dc.subject segmentation en dc.subject Athens [Attica] en dc.subject Attica en dc.subject Greece en dc.title Comparison of two fuzzy algorithms in geodemographic segmentation analysis: The fuzzy C-means and Gustafson-Kessel methods en dc.type journalArticle en
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