An Improved Feature Weighted K-means Clustering Algorithm

WANG Hui; SHEN Shi-lei

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An Improved Feature Weighted K-means Clustering Algorithm

更新时间：2026-09-04

- An Improved Feature Weighted K-means Clustering Algorithm
- Vol. 27, Issue 7, Pages: 161-163(2010)
- 作者机构：
  
  1. 河南大学计算机与信息工程学院,河南,开封,457004
  2. 河南大学机构计算中心,河南,开封,457004
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
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王慧, 申石磊. 一种改进的特征加权K-means聚类算法[J]. 微电子学与计算机, 2010, 27(7): 161-163,167.

WANG Hui, SHEN Shi-lei. An Improved Feature Weighted K-means Clustering Algorithm[J]. 2010, 27(7): 161-163.
王慧, 申石磊. 一种改进的特征加权K-means聚类算法[J]. 微电子学与计算机, 2010, 27(7): 161-163,167. DOI：

WANG Hui, SHEN Shi-lei. An Improved Feature Weighted K-means Clustering Algorithm[J]. 2010, 27(7): 161-163. DOI：

摘要

提出了一种改进的特征加权K-means聚类算法.该算法首先基于数据样本分布选取初始聚类中心

然后设计特征加权的K-means聚类算法.实验结果证明

该算法能产生质量较高的聚类结果

并且能处理数值、符号两类数据.

Abstract

An improved weighted K-means clustering algorithm is proposed. In the presented algorithm

firstly the original clustering center is obtained according to the data sample distribution. Then the improved K-means algorithm using the feature weights is designed. Experiment results have shown that the proposed clustering algorithm can produce high quality clustering steadily and deal with the symbolic data as well as the numeric data.

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