| 基于新相异度量的模糊K-Modes聚类算法 |
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| 引用本文： | 张月琴,陈彩棠.基于新相异度量的模糊K-Modes聚类算法[J].电脑开发与应用,2012,25(5):32-34. |
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| 作者姓名： | 张月琴 陈彩棠 |
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| 作者单位： | 太原理工大学计算机科学与技术学院,太原,030024 |
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| 摘 要： | 提出了一种基于新相异度量的模糊K-Modes算法。该算法假定不同属性对聚类结果有不同程度的影响,定义了新的属性值函数,以基于划分相似度的聚类精确度作为聚类结果的评价准则。通过真实数据的实验结果表明,新的基于相异度量的模糊K-Modes算法比传统的模糊K-Modes算法有更好的聚类效果。
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| 关 键 词： | K-Modes聚类算法 相异度量 分类属性 |
Fuzzy K-modes Clustering Algorithm Based on a New Dissimilarity Measure |
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| ZHANG Yue-qin
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CHEN Cai-tang.Fuzzy K-modes Clustering Algorithm Based on a New Dissimilarity Measure[J].Computer Development & Applications,2012,25(5):32-34. |
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| Authors: | ZHANG Yue-qin CHEN Cai-tang |
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| Affiliation: | (Department of Computer Science and Technology,Taiyuan University of Technology,Taiyuan 030024,China) |
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| Abstract: | A fuzzy K-Modes clustering algorithm based on New Dissimilarity Measure is presented for the different contribution of each attribute of the data set to the clustering.With a new function of the attribute value,the clustering accuracy based on the partition similitude is used to evaluate the clustering result.Experimental results on real life data show that the new fuzzy k-modes algorithm is superior to the standard fuzzy k-modes algorithm with respect to clustering accuracy. |
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| Keywords: | K-Modes clustering algorithm dissimilarity measure categorical attribute |
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