A new fuzzy clustering algorithm for optimally finding granular prototypes
International Journal of Approximate Reasoning
Classification of MPEG VBR video data using gradient-based FCM with divergence measure
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Classification of audio signals using gradient-based fuzzy c-means algorithm with divergence measure
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
Mathematical and Computer Modelling: An International Journal
A fuzzy multistage evolutionary (FUME) clustering technique
Pattern Recognition Letters
Satellite image classification using a divergence-based fuzzy c-means algorithm
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Automatic Unsupervised Segmentation Methods for MRI Based on Modified Fuzzy C-Means
Fundamenta Informaticae
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The uniform data function is a function which assigns to the output of the fuzzy c-means (Fc-M) or fuzzy isodata algorithm a number which measures the quality or validity of the clustering produced by the algorithm. For the preselected number of cluster c, the Fc-M algorithm produces c vectors in the space in which the data lie, called cluster centers, which represent points about which the data are concentrated. It also produces for each data point c-membership values, numbers between zero and one which measure the similarity of the data points to each of the cluster centers. It is these membership values which indicate how the point is classified. They also indicate how well the point has been classified, in that values close to one indicate that the point is close to a particular center, but uniformly low memberships indicate that the point has not been classified clearly. The uniform data functional (UDF) combines the memberships in such a way as to indicate how well the data have been classified and is computed as follows. For each data point compute the ratio of its smallest membership to its largest and then compute the probability that one could obtain a smaller ratio (indicating better classification) from a clustering of a standard data set in which there is no cluster structure. These probabilities are then averaged over the data set to obtain the values of the UDF.