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基于遗传算法的高维数据模糊聚类
引用本文:王宝文,阎俊梅,刘文远,石岩.基于遗传算法的高维数据模糊聚类[J].计算机工程与应用,2007,43(16):191-192.
作者姓名:王宝文  阎俊梅  刘文远  石岩
作者单位:1. 燕山大学,信息学院,河北,秦皇岛,066004
2. 日本九州东海大学,工程学院,信息系统工程系
基金项目:国家科技部高新技术计划项目 , 河北省科技计划 , 河北省博士科研项目
摘    要:提出了一种基于遗传算法的高维数据模糊聚类方法。引入了一个模糊非相似矩阵来表示高维样本之间的非相似程度,并将高维样本初始化到二维平面。利用遗传算法进行迭代优化二维样本的坐标值,实现二维样本之间的欧氏距离向样本间的模糊非相似度的趋近,使高维样本映射到二维平面。最后将得到的最优的二维样本利用模糊C-均值聚类(FCM)算法聚类,克服了聚类有效性对高维样本空间分布的依赖。实验仿真表明利用该方法有较好的聚类效果,且比用FCM算法直接聚类收敛速度快。

关 键 词:模糊聚类  模糊非相似矩阵  遗传算法  高维数据
文章编号:1002-8331(2007)16-0191-02
修稿时间:2006-10

High dimensional datas fuzzy clustering based on genetic algorithm
WANG Bao-wen,YAN Jun-mei,LIU Wen-yuan,SHI Yan.High dimensional datas fuzzy clustering based on genetic algorithm[J].Computer Engineering and Applications,2007,43(16):191-192.
Authors:WANG Bao-wen  YAN Jun-mei  LIU Wen-yuan  SHI Yan
Affiliation:1.Informatin Science and Engineering Institute of Yanshan University,Qinhuangdao, Hebei 066004,China ;2.Department of Information System Engineering,School of Engineering,Kyushu Tokai University,Japan
Abstract:A high dimensional datas fuzzy clustering method is presented based on genetic algorithm,by importing a fuzzy dissimilar matrix to express the dissimilar degree between any two datas,and initializing the high dimensional samples to two dimensional plane.And then iteratively optimize the coordinate value of two dimensional plane using genetic algorithm,which makes the euclidean distance between the two dimensional plane approximate to the fuzzy dissimilar degree between samples gradually,and the high dimensional samples are mapped into two dimensional plane.At last,using FCM algorithm to the two dimensional datas,avoids the dependence of the validity of clustering on the space distribution of high dimensional samples. Experimental results show that the method this paper proposed has more exact clustering result and faster convergence speed than FCM algorithm.
Keywords:fuzzy clustering  fuzzy dissimilar matrix  genetic algorithm  high dimensional datas
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