Performance analysis for clustering algorithms

There are lots of algorithms for optimal clustering. The main part of clustering algorithms includes the K-means, fuzzy c-means (FCM) and evolution algorithm. The main purpose of this paper was to research the performance and characteristics of these three types of algorithms. One criteria (clustering validity index), namely TRW, was used in the optimisation of classification and eight real-world datasets (glass, wine, ionosphere, biodegradation, connectionist bench, hill-valley, musk, madelon datasets), whose dimension became higher, were applied. We made a performance analysis and concluded that it was easy of the K-means and FCM to fall into a local minimum, and the hybrid algorithm was found more reliable and more efficient, especially on difficult tasks with high dimension.