Improved Initial Clustering Center Selection Method for k-means Algorithm

被引：0
|
作者：
Xie, Qingqing [1 ]
Jiang, He [1 ]
Han, Bing [1 ]
Wang, Dongyuan [1 ]
机构：
[1] QiLu Univ Technol, ShanDong Acad Sci, Jinan, Peoples R China
来源：
2018 EIGHTH INTERNATIONAL CONFERENCE ON INSTRUMENTATION AND MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC 2018) | 2018年
关键词：
clustering algorithm; initial center point; Pearson correlation; Randomness;
D O I：
10.1109/IMCCC.2018.00227
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Aiming at the Randomness about selecting the initial center point.This paper presents improvement scheme, in which one can select initial center point based on the Pearson correlation to avoid the randomness. After the experiment, the initial center point selected can effectively reduce the number of iterations of the clustering algorithm, and improve the efficiency of clustering algorithm. In the mean time, clustering results and the number of iterations has good stability.
引用
收藏
页码：1092 / 1095
页数：4
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