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I m trying to perform image segmentation using rapidminer's clustering algorithms. Except K-means, who completes execution in aproximatelly 3-4 minutes, other methods (EM, k-medoids, Kernel k-means) never seem to converge (although on a Q6600 with 2GB, rapidminer never uses more than 30% of my cpu).
My data are simple features derived from pixels such as texture, magnitude, gradient etc all normalized to 0-1 (for each 300x400 image, a 300x400x3 feature matrix is extracted).
Do i need more powerfull cpu/memory or some kind of different normalization/preprocessing specifically for these algorithms??
Thnk you & sorry for the long msg (O>o)