Adaptive Peak Environmental Density Clustering Algorithm in Cloud Computing Technology.

In order to improve the clustering ability of grid sparse unbalanced cloud data sets, an adaptive environment density peak clustering algorithm based on cloud computing technology is proposed. Firstly, the storage structure model of grid sparse unbalanced cloud data set is constructed, and the structural reorganization of grid sparse unbalanced cloud data set is carried out by combining the feature space reorganization technology. Meanwhile, the rough feature quantity of the grid sparse unbalanced cloud data set is extracted, and then cloud fusion and peak feature clustering of the data set are carried out according to the grid block distribution of the data set. The peak feature quantity of the grid sparse unbalanced cloud data set is extracted for distributed detection of binary semantic features of the data. Finally, according to semantic fusion and feature clustering results, regression analysis and support vector machine learning are used to optimize clustering of grid sparse unbalanced cloud data sets. Experimental results show that this method has good convergence, low data misclassification rate, and good clustering performance of peak environmental density. [ABSTRACT FROM AUTHOR]