J4 ›› 2006, Vol. 28 ›› Issue (11): 56-59.
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蒋盛益[1,2] 李庆华[2]
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摘要:
本文针对κ-modes算法在类的表示方面存在的不足,提出用摘要信息来表示一个类,并给出了一种适用于混合属性的距离定义,得到增强的κ-means算法——κ-summary算法 。理论分析和实验结果表明,κ-sumnlary算法较κ—modes算法和κ-prototypes算法具有更好的精度。
关键词: 数据挖掘 聚类算法 &kappa, -summary 算法
Abstract:
As for the shortcomings of the κ-modes algorithm in the representation of class, we present a method for the representation of class with summary information. A distance definition for mixed attributes is proposed in this paper. Based on the distance definition, we extend the κ-means algorithm and ppresent the κ-summary algorithm, Theoretical analyses and experimental results demonstrate that the κ-summary algorithm can create more accurate classs results than the κ- modes algorithm and the κ-prototypes algorithm
Key words: (data mining;clustering algorithm;κ-summary algorithm)
蒋盛益[1,2] 李庆华[2]. 一种增强的κ-means聚类算法[J]. J4, 2006, 28(11): 56-59.
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http://joces.nudt.edu.cn/CN/Y2006/V28/I11/56