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G. Beni, X. Liu, "A Least Biased Fuzzy Clustering Method," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 9, pp. 954960, September, 1994.  
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@article{ 10.1109/34.310694, author = {G. Beni and X. Liu}, title = {A Least Biased Fuzzy Clustering Method}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {16}, number = {9}, issn = {01628828}, year = {1994}, pages = {954960}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.310694}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  A Least Biased Fuzzy Clustering Method IS  9 SN  01628828 SP954 EP960 EPD  954960 A1  G. Beni, A1  X. Liu, PY  1994 KW  entropy; fuzzy set theory; pattern recognition; least biased fuzzy clustering method; minimal biases; maximum entropy principle; clustering entropy; hard partitions; fuzzy partitions; multiscale analysis; relative stability; cluster structure VL  16 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A new operational definition of cluster is proposed, and a fuzzy clustering algorithm with minimal biases is formulated by making use of the maximum entropy principle to maximize the entropy of the centroids with respect to the data points (clustering entropy). The authors make no assumptions on the number of clusters or their initial positions. For each value of an adimensional scale parameter /spl beta/', the clustering algorithm makes each data point iterate towards one of the cluster's centroids, so that both hard and fuzzy partitions are obtained. Since the clustering algorithm can make a multiscale analysis of the given data set one can obtain both hierarchy and partitioning type clustering. The relative stability with respect to /spl beta/' of each cluster structure is defined as the measurement of cluster validity. The authors determine the specific value of /spl beta/' which corresponds to the optimal positions of cluster centroids by minimizing the entropy of the data points with respect to the centroids (clustered entropy). Examples are given to show how this least biased method succeeds in getting perceptually correct clustering results.
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