一种基于Huffman树的FCM聚类算法

被引：2

作者：

肖满生

周丽娟

文志诚

机构：

[1] 湖南工业大学计算机学院

来源：

数据分析与知识发现 | 2018年 / 07期

基金：

湖南省自然科学基金;

关键词：

样本密度; 相异度; Huffman树; 隶属度;

D O I：

暂无

中图分类号：

TP311.13 [];

学科分类号：

1201 ;

摘要：

【目的】解决传统的FCM算法随机选取初始聚类中心、对噪声敏感、只适合均衡分布的样本聚类问题。【方法】提出一种基于Huffman树的FCM新算法,该算法设计一种高密度样本的相异度矩阵构建Huffman树并获取初始聚类中心,进而给出非归一化约束的样本隶属度函数。【结果】通过人造样本及图像数据集、UCI数据集的实验对比结果表明,算法在聚类精度、运算时间等指标上比基于高斯核FCM算法及传统FCM算法更有优势。【局限】仅凭实验或经验确定样本密度调节因子?,尚缺乏理论依据。【结论】本研究在现实生活中对含有大量噪声样本及样本分布非均衡的数据集聚类有一定的实际应用价值。

引用

页码：81 / 88

页数：8

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