Abstract:Clustering method is a single-objective optimal method in nature,a prior specified number of clusters and the initial cluster centroids must be given in advance,and different choices of initial guesses of cluster centroids can lead to different partitions of the same data. The new representation proposed in this paper deals with the partitional clustering problem by regarding it as a multi-objective optimal problem;in this approach the niche Pareto genetic algorithm is used to solve the problem. Aiming at clustering problem,a linked-list based encoding scheme and accordingly genetic operators are presented. With the introduction of niche technique and Pareto dominant set theory,the optimal partitions for all possible numbers of clusters in the Pareto optimal set returned by a single GA run are obtained. The performance of the proposed approach has been tested using artificial data and the data form real rock mass of the shiplock high slope of the Three Gorges Project. The obtained results are promising and demonstrate the applicability and effectiveness of the proposed approach.