New k-means clustering center select algorithm

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (35): 132-134.

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

New k-means clustering center select algorithm

HUANG Min1，HE Zhongshi2，XING Xinlai2，CHEN Ying1

1.Department of Information Engineering，Chongqing Industry & Trade Polytechnic，Chongqing 408300，China
2.College of Computer Science，Chongqing University，Chongqing 400044，China

Received:1900-01-01 Revised:1900-01-01 Online:2011-12-11 Published:2011-12-11

一种新的k-means聚类中心选取算法

黄敏1，何中市2，邢欣来2，陈英1

1.重庆工贸职业技术学院信息工程系，重庆 408300
2.重庆大学计算机学院，重庆 400044

Abstract

Abstract: A part of the existing algorithm is improved.Through computing the distance between data object to count the density parameter of every data object，the biggest density parameter data objects are chosen as the initial clustering centers.When more than one biggest density parameter，the solution how to select the biggest density parameter is proposed，k initial clustering centers are found.And a new k-means clustering center algorithm is proposed.The experimental result proves the improved algorithm can get higher accuracy.

Key words: k-means algorithm, clustering center, density parameter

摘要： 在2010年提出已有的k-means聚类中心选取算法的基础上进行改进。通过计算样本间的距离求出每个样本的密度参数，选取最大密度参数值所对应的样本作为初始聚类中心。当最大密度参数值不惟一时，提出合理选取最大密度参数值的解决方案，依次求出k个初始聚类中心点，由此提出了一种新的k-means聚类中心选取算法。实验证明，提出的算法与对比算法相比具有更高的准确率。

关键词: k-means算法, 聚类中心, 密度参数

HUANG Min1，HE Zhongshi2，XING Xinlai2，CHEN Ying1. New k-means clustering center select algorithm[J]. Computer Engineering and Applications, 2011, 47(35): 132-134.

黄敏1，何中市2，邢欣来2，陈英1 . 一种新的k-means聚类中心选取算法[J]. 计算机工程与应用, 2011, 47(35): 132-134.

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New k-means clustering center select algorithm

一种新的k-means聚类中心选取算法

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