Using fuzzy clustering methods for delineating urban housing submarkets

This study investigates whether a fuzzy clustering method is of any practical value in delineating urban housing submarkets relative to clustering methods based on classic (or crisp) set theory. A fuzzy c-means algorithm is applied to obtain fuzzy set membership degree of census tracts to housing submarkets defined within a metropolitan area. Issues of choosing algorithm parameters are discussed on the basis of applying fuzzy clustering to 85 metropolitan areas in the U.S. The comparison between results of fuzzy clustering and those of crisp set counterpart shows that fuzzy clustering yields statistically more desirable clusters. Categories and Subject Descriptors I.5.3 [Pattern Recognition] General Terms Algorithms, Performance Keywords Fuzzy clustering, Data mining, Housing submarket, GIS

• Type: Conference
• Year: 2007
• Where: GIS
• Authors: Sungsoon Hwang, Jean-Claude Thill
• Keywords: Database, Fuzzy Clustering, Fuzzy Clustering Method, Fuzzy Clustering Yields, GIS 2007