Fast fuzzy clustering
This paper presents a multistage random sampling fuzzy c-means-based clustering algorithm, which significantly reduces the computation time required to partition a data set into c classes. A series of subsets of the full data set are used to create initial cluster centers in order to provide an approximation to the final cluster centers. The quality of the final partitions is equivalent to those created by fuzzy c-means. The speed-up is normally a factor of 2–3 times, which is especially significant for high-dimensional spaces and large data sets. Examples of the improved speed of the algorithm in two multi-spectral domains, magnetic resonance image segmentation and satellite image segmentation, are given. The results are compared with fuzzy c-means in terms of both the time required and the final resulting partition. Significant speedup is shown in each example presented in the paper. Further, the convergence properties of fuzzy c-means are preserved.