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一种新的模糊C均值聚类算法 被引量：3

A New Fuzzy C-Means Algorithm
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摘要 传统的模糊C均值聚类算法及其变型在聚类过程中都假设所有的属性对聚类贡献相同,所以很难发现隐藏在部分属性中的类结构,也难以识别出重要属性.在实际应用中,噪声属性较为常见,并且会影响正常的聚类过程.鉴于以上原因,提出了一种新的基于属性加权的模糊C均值聚类算法,通过对人工数据和实际数据的聚类测试结果,证实了该算法的有效性. Because the Standard fuzzy C-means algorithm and most of its extensions treat all the attributes equally in clustering process,they can＇t discover the latent cluster structure.However,some attributes contribute more than others in some cases.Considering this,this paper proposes a new attribute weighted fuzzy C-means algorithm.The results of the numerical experiment prove the validity of the new algorithm.
作者 李翠霞 谭营军
机构地区 郑州大学软件技术学院 河南职业技术学院信息工程系
出处 《河南大学学报（自然科学版）》 CAS 北大核心 2011年第2期201-205,共5页 Journal of Henan University:Natural Science
关键词 权值 属性加权 模糊指数 模糊C均值聚类算法 weight attribute weighted fuzzy exponent fuzzy C-means algorithm
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献16

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二级参考文献18

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共引文献96

同被引文献20

引证文献3

二级引证文献16

河南大学学报（自然科学版）
2011年 第2期

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