Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 10
2988-2993,3024

Partition clustering algorithm based on MapReduce and improved density peak

Huang Xueyu
Xiang Chi
Tao Tao
School of Information Engineering, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
DOI: 10.19734/j.issn.1001-3695.2021.03.0093

Abstract

Aiming at clustering algorithm based on partition to randomly select the initial cluster center, which leads to the sensitivity of the initial center, unstable clustering result, low cluster efficiency, etc., this paper proposed a partition clustering algorithm based on MapReduce framework and improved density peak, named MR-IDPACA. Firstly, this paper defined a new local density calculation method by natural nearest neighbors, and then searched for the peak point of the sample density as the initial cluster center of the partitioning clustering algorithm. Secondly, in viewed of the complex running time of the algorithm under large-scale data, it proposed an algorithm based on E2LSH, named KLSH. In this method, the data was partitioned and combined with the MapReduce framework to search the initial cluster centers in parallel, which effectively reduced the running time of the algorithm when searching for the initial cluster centers. Next, for the data skew problem in the MapReduce framework, it proposed the ME strategy to divide the intermediate data into multi-segment equilibrium to improve the efficiency of the algorithm. Finally, parallel clustering under the MapReduce framework to obtain the final clustering result. The experiment shows that the MR-IDPACA algorithm has higher accuracy and stronger stability in a single-machine environment, and the cluster performance also has a better speedup ratio and running time, and the clustering effect has been improved.

Foundation Support

国家重点研发计划项目(2020YFB1713700)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.03.0093
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 10
Section: Algorithm Research & Explore
Pages: 2988-2993,3024
Serial Number: 1001-3695(2021)10-017-2988-06

Publish History

[2021-10-05] Printed Article

Cite This Article

黄学雨, 向驰, 陶涛. 基于MapReduce和改进密度峰值的划分聚类算法 [J]. 计算机应用研究, 2021, 38 (10): 2988-2993,3024. (Huang Xueyu, Xiang Chi, Tao Tao. Partition clustering algorithm based on MapReduce and improved density peak [J]. Application Research of Computers, 2021, 38 (10): 2988-2993,3024. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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