Abstract：In cluster analysis, Fuzzy K-Means (FKM) algorithm is one of the most widely used methods. However, FKM algorithm is much more sensitive to the initialization, and easy to fall into local optimum. For this purpose, this paper presents a clonal selection based new algorithm for fuzzy clustering analysis, for global optimization. Since the clonal operator can combine the evolutionary search and random search, and incorporate the global search with local search, by the clonal operation on candidate solutions, the new algorithm can quickly obtain the global optimum. The experimental results with synthetic data and IRIS real data illustrate the effectiveness of the new algorithm.
2005, Vol. 27 
