Abstract A clustering algorithm named Fuzzy k-Plane Clustering (FkPC) is proposed by introducing fuzzy membership into the prevalent k-Plane Clustering (kPC). From the view of prototype selection, FkPC substitutes hyperplanes for points as the prototype, which is similar with kPC. Meanwhile, FkPC represents the membership between the points and its central hyperplanes much more clearly than kPC, due to the introduction of fuzzy membership into its objective function. Experimental results of both artificial and UCI datasets have proved the clustering validity of FkPC, and they also reveal that besides the similarity metric, the expression of prototype also plays a crucial role in clustering.
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2007, Vol. 20 
