Initializing cluster method with motion of grid’s center

doi:10.3778/j.issn.1002-8331.2010.13.040

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (13): 135-138.DOI: 10.3778/j.issn.1002-8331.2010.13.040

• 数据库、信号与信息处理 • Previous Articles Next Articles

Initializing cluster method with motion of grid’s center

WEI Xiang，XU Hai-cheng，WANG Hong-xiao

Department of Computer Science，University of Honghe，Mengzi，Yunnan 661100，China

Received:2008-11-27 Revised:2009-02-02 Online:2010-05-01 Published:2010-05-01
Contact: WEI Xiang

网格质心运动的聚类初始化方法

韦相，许海成，王红晓

红河学院计算机科学与技术系，云南蒙自 661100

通讯作者: 韦相

Abstract

Abstract: In order to solve the problems that how to set the number of classifications and the initial cluster center for k-means algorithm that is regarded as exemplifying of partitioning algorithms，an initial algorithm based on center motion is proposed.After gridding and defining mass of grid，motion theorem of the mass center is used to extract the centers of clustering samples，and then classifications are determined.Experiments on synthetic datasets show that compared with current approaches，this method can extract the centers of clustering samples more validly，restrain noise，improve clustering effect，and have good efficiency for cluster analysis.

Key words: grid center, cluster center, initialization, mass of grid

摘要： 针对以k-means为代表的分割聚类算法初始参数的很难选取这一难题，提出基于网格质心运动的初始化算法。划分网格后，定义网格的质量，利用物质质心运动理论，提取样本的聚类中心，并由此确定样本分类数k。实验表明，该算法可以有效地提取初始聚类中心，消除噪声点，可以提高后续聚类分析的效果和效率。

关键词: 网格质心, 聚类中心, 初始化, 网格质量

CLC Number:

TP39

WEI Xiang，XU Hai-cheng，WANG Hong-xiao. Initializing cluster method with motion of grid’s center[J]. Computer Engineering and Applications, 2010, 46(13): 135-138.

韦相，许海成，王红晓. 网格质心运动的聚类初始化方法[J]. 计算机工程与应用, 2010, 46(13): 135-138.

[1]	WANG Fuyin, ZHANG Desheng, ZHANG Xiao. Adaptive Density Peaks Clustering Algorithm Combining with Whale Optimization Algorithm [J]. Computer Engineering and Applications, 2021, 57(3): 94-102.
[2]	WANG Zilong, LI Jin, SONG Yafei. Improved K-means Algorithm Based on Distance and Weight [J]. Computer Engineering and Applications, 2020, 56(23): 87-94.
[3]	YANG Junchuang, ZHAO Chao. Survey on K-Means Clustering Algorithm [J]. Computer Engineering and Applications, 2019, 55(23): 7-14.
[4]	WANG Zhen, LI Xufei. Self-Adaptive Artificial Bee Colony Algorithm for Constrained Optimization Problem [J]. Computer Engineering and Applications, 2019, 55(15): 47-58.
[5]	WU Xuan, WANG Yan, CHEN Xing. Fast Image Segmentation Model Without Initial Contour [J]. Computer Engineering and Applications, 2019, 55(11): 167-171.
[6]	DU Pei, CHENG Xiaorong. Comparative Density Peaks Clustering Based on [K]-Nearest Neighbors [J]. Computer Engineering and Applications, 2019, 55(10): 161-168.
[7]	ZHU Zhenguo, FENG Yingzhu. Clustering center selection and clustering based on data field [J]. Computer Engineering and Applications, 2018, 54(8): 131-136.
[8]	WANG Yang1, ZHANG Guizhu2. Automatically determine density of cluster center of peak algorithm [J]. Computer Engineering and Applications, 2018, 54(8): 137-142.
[9]	XIAO Suqiong1，2, LUO Ke1，2. Krill Herd optimization algorithm with reverse-learning and local-learning behavior [J]. Computer Engineering and Applications, 2018, 54(18): 34-39.
[10]	DONG Peifang1, ZHANG Zhi’an1, MEI Xinhu2, ZHU Shuo1. Reinforcement learning path planning algorithm based on gravitational potential field and trap search [J]. Computer Engineering and Applications, 2018, 54(16): 129-134.
[11]	YANG Yatao1, ZHANG Songtao1, LI Zichen1，2, WANG Peidong1, QU Ming1. Design and implementation for physical layer of serial ATA revision 3.0 based on FPGA [J]. Computer Engineering and Applications, 2017, 53(20): 38-42.
[12]	YE Hongtao1，2, PI Qianying1，2. Multi-strategy particle swarm optimization algorithm and its convergence analysis [J]. Computer Engineering and Applications, 2016, 52(24): 171-177.
[13]	DANG Xiaochao1，2, MAO Pengxin1, HAO Zhanjun1，2. Network traffic clustering algorithm based on quick solution of GMM [J]. Computer Engineering and Applications, 2015, 51(8): 96-101.
[14]	LI Min1，2, SONG Yuecong1, WU Bin3, PENG Bao3. Three-dimension human motion tracking based improved particle filter on Beowulf cluster system [J]. Computer Engineering and Applications, 2015, 51(14): 17-22.
[15]	MAO Zhengchong, XU Hao, WANG Dan. Application of geometric active contours in target tracking [J]. Computer Engineering and Applications, 2015, 51(13): 176-180.

Initializing cluster method with motion of grid’s center

网格质心运动的聚类初始化方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics