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 str...

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Bibliographic Details
Published in:Automatic control and computer sciences Vol. 57; no. 3; pp. 258 - 266
Main Author: Zhao, Nannan
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 01.06.2023
Springer Nature B.V
Subjects:
Algorithms
Cloud computing
Clustering
Computer Science
Control Structures and Microprogramming
Data mining
Datasets
Deep learning
Density
Machine learning
Optimization techniques
Regression analysis
Scheduling
Semantics
Sparsity
Support vector machines
cloud data
cloud computing technology, self-adaptive, environmental density, peak clustering algorithm
ISSN:0146-4116, 1558-108X
Online Access:Get full text
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Summary:— 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.
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ISSN:0146-4116
1558-108X
DOI:10.3103/S0146411623030112