Clustering of symbolic data using the assignment-prototype algorithm


This paper shows a fuzzy relational clustering method in order to perform the clustering of symbolic data. The presented method yields a fuzzy partition and prototype for each cluster by optimizing an adequacy criterion based on suitable dissimilarity measures. This work considers two volume-based measures that may be applied to data described by set-valued… (More)
DOI: 10.1109/IJCNN.2009.5178764


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