This paper proposes a new improved mountain clustering technique, which is compared with some of the existing techniques such as K-Means, FCM, EM and Modified Mountain Clustering. The performance of all these clustering techniques towards color image segmentation is compared in terms of cluster entropy as a measure of information and observed via computational complexity. The cluster entropy is heuristically determined, but is found to be effective in forming correct clusters as verified by visual assessment.
