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DOI:
Journal of Software:2001.12(8):1107-1119

数值型和分类型混合数据的模糊K-Prototypes聚类算法
陈宁,陈安,周龙骧
(中国科学院数学与系统科学研究院北京 100080;中国科学院科技政策与管理科学研究所北京 100080 中国科学院软件研究所北京 100080)
Fuzzy K-Prototypes Algorithm for Clustering Mixed Numericand Categorical Valued Data
CHEN Ning,CHEN An,ZHOU Long xiang
()
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> 中文摘要: 由于数据库经常同时包含数值型和分类型的属性,因此研究能够处理混合型数据的聚类算法无疑是很重要的.讨论了混合型数据的聚类问题,提出了一种模糊K-prototypes算法.该算法融合了K-means和K-modes对数值型和分类型数据的处理方法,能够处理混合类型的数据.模糊技术体现聚类的边界特征,更适合处理含有噪声和缺失数据的数据库.实验结果显示,模糊算法比相应的确定算法得到的结果准确度高.
Abstract:The capacity of dealing with mixed numeric and categorical valued data is undoubtedly important for clustering algorithms because there is usually a mixture of numeric and categorical valued attributes in real databases. The use of fuzzy techniques makes clustering algorithms robust against noise and missing values in the databases. In this paper, a fuzzy kprototypes algorithm integrating k-means and k-modes algorithm is presented and is used to mixed databases. Experiments on several real databases demonstrategythat fuzzy algorithm can get better result than the corres ponding hard algorithm.Some properries of fuzzt k-prototypes algorithm are also discussed.
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基金项目:Supported by the National Natural Science Foundation of China under Grant No. 69983011(国家自然科学基金) Supported by the National Natural Science Foundation of China under Grant No. 69983011(国家自然科学基金)
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陈宁,陈安,周龙骧.数值型和分类型混合数据的模糊K-Prototypes聚类算法.软件学报,2001,12(8):1107-1119

CHEN Ning,CHEN An,ZHOU Long xiang.Fuzzy K-Prototypes Algorithm for Clustering Mixed Numericand Categorical Valued Data.Journal of Software,2001,12(8):1107-1119