k-Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

• Published in: Complexity (New York, N.Y.) Vol. 2021; no. 1
• Main Authors: Wu, Chunqiong, Yan, Bingwen, Yu, Rongrui, Yu, Baoqin, Zhou, Xiukao, Yu, Yanliang, Chen, Na
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi 2021; John Wiley & Sons, Inc; Wiley
• Subjects: Accuracy; Algorithms; Big Data; Cluster analysis; Clustering; Computer networks; Data analysis; Data mining; Design; Distributed processing; Efficiency; Energy consumption; Explosive plating; Knowledge discovery; Mathematical analysis; Multiprocessing; Optimization; Parallel processing; Random sampling; Stress concentration; Vector quantization
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2021/9446653

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.