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dc.creatorGrekousis, G.en
dc.creatorThomas, H.en
dc.date.accessioned2015-11-23T10:29:12Z
dc.date.available2015-11-23T10:29:12Z
dc.date.issued2012
dc.identifier10.1016/j.apgeog.2011.11.004
dc.identifier.issn1436228
dc.identifier.urihttp://hdl.handle.net/11615/28213
dc.description.abstractClustering 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.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-84155179103&partnerID=40&md5=f947e6c19daad8c677fb8941d1b865f9
dc.subjectFuzzy C-meansen
dc.subjectGeodemographic segmentationen
dc.subjectGustfson-kessel algorithmen
dc.subjectalgorithmen
dc.subjectcensusen
dc.subjectcluster analysisen
dc.subjectdata seten
dc.subjectexperimental studyen
dc.subjectfuzzy mathematicsen
dc.subjectmetropolitan areaen
dc.subjectsegmentationen
dc.subjectAthens [Attica]en
dc.subjectAtticaen
dc.subjectGreeceen
dc.titleComparison of two fuzzy algorithms in geodemographic segmentation analysis: The fuzzy C-means and Gustafson-Kessel methodsen
dc.typejournalArticleen


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