Cluster analysis of fuzzy C-mean algorithm based on different similarity estimation distances

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (18): 17-18.

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Cluster analysis of fuzzy C-mean algorithm based on different similarity estimation distances

LI Zhong，YUAN Jinsha

School of Electrical and Electronic Engineering，North China Electric Power University，Baoding，Hebei 071003，China

Received:1900-01-01 Revised:1900-01-01 Online:2011-06-21 Published:2011-06-21

不同相似度测量方式的模糊C均值聚类分析

李中，苑津莎

华北电力大学电气与电子工程学院，河北保定 071003

Abstract

Abstract: Clustering is a key technology widely used in machine learning，pattern recognition，and data mining.Based on different similarity estimation methods，Fuzzy C-Means（FCM） clustering simulation experiments are implemented on three UCI known data sets，test results are analyzed from both sides of accuracy and running efficiency，and it can give a valuable reference for data clustering．

Key words: cluster analysis, Fuzzy C-Means（FCM）, similarity

摘要： 聚类技术是机器学习、模式识别及数据挖掘等领域中的一个重要研究内容。采用不同相似度测量方式，应用标准模糊C均值聚类算法在UCI的三个知名数据集上完成聚类实验，从正确率和运行效率两个方面对比分析其性能，为聚类分析研究提供了有益的参考。

关键词: 聚类分析, 模糊C均值, 相似度

LI Zhong，YUAN Jinsha. Cluster analysis of fuzzy C-mean algorithm based on different similarity estimation distances[J]. Computer Engineering and Applications, 2011, 47(18): 17-18.

李中，苑津莎. 不同相似度测量方式的模糊C均值聚类分析[J]. 计算机工程与应用, 2011, 47(18): 17-18.

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Cluster analysis of fuzzy C-mean algorithm based on different similarity estimation distances

不同相似度测量方式的模糊C均值聚类分析

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