A fuzzy relative of the k-medoids algorithm with application to web document and snippet clustering

This paper presents new algorithms (fuzzy e-methods (FCMdd) and fuzzy c trimmed medoids (FCTMdd)) for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total dissimilarity within each cluster is minimized. A comparison of FCMdd with the relational fuzzy c-means algorithm shows that FCMdd is much faster. We present examples of applications of these algorithms to web document and snippet clustering.

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