Efficient Distributed Database Clustering Algorithm for Big Data Processing

When clustering efficient distributed database, the conventional algorithm has long time cost and low clustering accuracy. To solve the above problems, an efficient distributed database clustering algorithm for big data processing is designed. Calculating the eigenvalues of the database, and linking the efficient distributed database with similar characteristics. The cross correlation matrix is used to ensure the consistency of cluster label. To improve the performance of K-means algorithm, input the database to be clustered, output $k$ clustering centers, and divide the clustering groups. Mapping database to clustering center, clustering low dimensional big data. Experimental results show that the proposed algorithm can reduce the running time and mean square error of data clustering, and improve the efficiency and accuracy of clustering.