k-Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform
At present, the explosive growth of data and the mass storage state have brought many problems such as computational complexity and insufficient computational power to clustering research. The distributed computing platform through load balancing dynamically configures a large number of virtual comp...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9446653 |
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| _version_ | 1850209199359787008 |
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| author | Chunqiong Wu Bingwen Yan Rongrui Yu Baoqin Yu Xiukao Zhou Yanliang Yu Na Chen |
| author_facet | Chunqiong Wu Bingwen Yan Rongrui Yu Baoqin Yu Xiukao Zhou Yanliang Yu Na Chen |
| author_sort | Chunqiong Wu |
| collection | DOAJ |
| description | At present, the explosive growth of data and the mass storage state have brought many problems such as computational complexity and insufficient computational power to clustering research. The distributed computing platform through load balancing dynamically configures a large number of virtual computing resources, effectively breaking through the bottleneck of time and energy consumption, and embodies its unique advantages in massive data mining. This paper studies the parallel k-means extensively. This article first initializes random sampling and second parallelizes the distance calculation process that provides independence between the data objects to perform cluster analysis in parallel. After the parallel processing of the MapReduce, we use many nodes to calculate distance, which speeds up the efficiency of the algorithm. Finally, the clustering of data objects is parallelized. Results show that our method can provide services efficiently and stably and have good convergence. |
| format | Article |
| id | doaj-art-957bb936754a4c09942848e187708ff5 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-957bb936754a4c09942848e187708ff52025-08-20T02:10:04ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/94466539446653k-Means Clustering Algorithm and Its Simulation Based on Distributed Computing PlatformChunqiong Wu0Bingwen Yan1Rongrui Yu2Baoqin Yu3Xiukao Zhou4Yanliang Yu5Na Chen6Business College, Yango University, Fuzhou, Fujian Province 350015, ChinaBusiness College, Yango University, Fuzhou, Fujian Province 350015, ChinaBusiness College, Yango University, Fuzhou, Fujian Province 350015, ChinaBusiness College, Yango University, Fuzhou, Fujian Province 350015, ChinaBusiness College, Yango University, Fuzhou, Fujian Province 350015, ChinaBusiness College, Yango University, Fuzhou, Fujian Province 350015, ChinaBig Data Business Intelligence Engineering Research Center, Fujian University, Fuzhou, Fujian Province 350015, ChinaAt present, the explosive growth of data and the mass storage state have brought many problems such as computational complexity and insufficient computational power to clustering research. The distributed computing platform through load balancing dynamically configures a large number of virtual computing resources, effectively breaking through the bottleneck of time and energy consumption, and embodies its unique advantages in massive data mining. This paper studies the parallel k-means extensively. This article first initializes random sampling and second parallelizes the distance calculation process that provides independence between the data objects to perform cluster analysis in parallel. After the parallel processing of the MapReduce, we use many nodes to calculate distance, which speeds up the efficiency of the algorithm. Finally, the clustering of data objects is parallelized. Results show that our method can provide services efficiently and stably and have good convergence.http://dx.doi.org/10.1155/2021/9446653 |
| spellingShingle | Chunqiong Wu Bingwen Yan Rongrui Yu Baoqin Yu Xiukao Zhou Yanliang Yu Na Chen k-Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform Complexity |
| title | k-Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform |
| title_full | k-Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform |
| title_fullStr | k-Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform |
| title_full_unstemmed | k-Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform |
| title_short | k-Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform |
| title_sort | k means clustering algorithm and its simulation based on distributed computing platform |
| url | http://dx.doi.org/10.1155/2021/9446653 |
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