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In the process of clustering, for the reason that the randomness of the class-center selection may lead to the phenomenon that the selected class-center deviates from the data set, or the class-center is too centralized, the proposed algorithm selected the class-center for two times: it screened out the class-centers which have too small density or have small distances between pairs of class-centers, and the algorithm did not allow them to participate in clustering. Then the algorithm continued to cluster the remaining class-centers. In order to make the algorithm get the optimal class-center quickly, it proposed an improved clustering criterion function to penalize the number of clusters dynamically. In order to evaluate the performance of the proposed algorithm on clustering problems, it carried out experiments on two types of data sets. Compared with the other three existing automatic clustering algorithms, simulation experiments show that the proposed algorithm can obtain better clustering results, which validates the effectiveness of the proposed strategies. [ABSTRACT FROM AUTHOR]

Aiming at the problem of slow speed and low accuracy of high dimensional data clustering in big data environment,this paper proposed a fast automatic clustering algorithm for big data( FACABD). Firstly,it realized the normalization and row dimension reduction for the large data set by means of spectral clustering algorithm. Second,it proposed a particle swarm optimization algorithm for fast regional evolution( FRE-PSO),which could improve the convergence speed and realized the line dimension reduction. And then it introduced the fuzzy cluster membership degree base to automatically discovery the cluster number. Finally,it realized fast automatic clustering by the FRE-PSO and fuzzy clustering algorithm. The experiments on multiple,different data sets show that the algorithm can contain the clustering results quickly and automatically by mining data itself,and it can effectively improve the speed and accuracy than the other clustering algorithms. [ABSTRACT FROM AUTHOR]