A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A new approach for measuring the validity of the fuzzy c-means algorithm
Advances in Engineering Software
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
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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.