Add to ORKGThis 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
Identifying buildings for safety purposes is critical to anticipate unforeseen scenarios during a di...
Abstract — We describe an interactive method to generate a set of fuzzy clusters for a given data se...
Day by day huge amounts data are produced, and evaluation of these data becomes more difficult. The ...
This study investigates whether a fuzzy clustering method is of any practical value in delineating u...
It has long been argued that the housing market is spatially compartmentalized within a metropolitan...
It has long been argued that the housing market is spatially subdivided within an urban area. The ar...
The aim of this paper has twofold: i) to explore the fundamental concepts and methods of neighborhoo...
A main obstacle to accurate prediction is often the heterogeneous nature of data. Existing studies h...
This study argues that the concept of fuzzy urban sets is particularly well suited to analyzing aspe...
The rapid urbanization of cities has a bane in the form road accidents that cause extensive damage t...
A huge effort has already been made to prove the existence of housing market segments, as well as ho...
In order to arrive at objective conclusions from a market survey, many quantitative methods can be u...
A hard partition clustering algorithm assigns equally distant points to one of the clusters, where e...
This master thesis deals with cluster analysis, more specifically with clustering methods that use f...
Cluster analysis is highly advantageous as it provides “relatively distinct” (or heterogeneous) clu...
Identifying buildings for safety purposes is critical to anticipate unforeseen scenarios during a di...
Abstract — We describe an interactive method to generate a set of fuzzy clusters for a given data se...
Day by day huge amounts data are produced, and evaluation of these data becomes more difficult. The ...
This study investigates whether a fuzzy clustering method is of any practical value in delineating u...
It has long been argued that the housing market is spatially compartmentalized within a metropolitan...
It has long been argued that the housing market is spatially subdivided within an urban area. The ar...
The aim of this paper has twofold: i) to explore the fundamental concepts and methods of neighborhoo...
A main obstacle to accurate prediction is often the heterogeneous nature of data. Existing studies h...
This study argues that the concept of fuzzy urban sets is particularly well suited to analyzing aspe...
The rapid urbanization of cities has a bane in the form road accidents that cause extensive damage t...
A huge effort has already been made to prove the existence of housing market segments, as well as ho...
In order to arrive at objective conclusions from a market survey, many quantitative methods can be u...
A hard partition clustering algorithm assigns equally distant points to one of the clusters, where e...
This master thesis deals with cluster analysis, more specifically with clustering methods that use f...
Cluster analysis is highly advantageous as it provides “relatively distinct” (or heterogeneous) clu...
Identifying buildings for safety purposes is critical to anticipate unforeseen scenarios during a di...
Abstract — We describe an interactive method to generate a set of fuzzy clusters for a given data se...
Day by day huge amounts data are produced, and evaluation of these data becomes more difficult. The ...