| 一种改进的K-means算法最佳聚类数确定方法 |
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| 引用本文： | 边鹏,赵妍,苏玉召.一种改进的K-means算法最佳聚类数确定方法[J].现代图书情报技术,2011(9). |
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| 作者姓名： | 边鹏 赵妍 苏玉召 |
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| 作者单位： | 中国科学院国家科学图书馆;中国科学院研究生院;郑州航空工业管理学院计算机科学与应用系; |
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| 摘 要： | 对BWP方法进行研究,从嵌入式NSTL个性化推荐的文本聚类需求入手,分析BWP方法的不足,提出一种改进的K-means算法最佳聚类数确定方法。对单一样本类的类内距离计算方法进行优化,扩展BWP方法适用的聚类数范围,使原有局部最优的聚类数优化为全局最优。实验结果可以验证该方法具有良好性能。
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| 关 键 词： | K-means聚类 聚类数 文本聚类 推荐系统 |
An Improved Method for Determining Optimal Number of Clusters in K - means Clustering Algorithm |
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| Bian Peng Zhao Yan Su Yuzhao.An Improved Method for Determining Optimal Number of Clusters in K - means Clustering Algorithm[J].New Technology of Library and Information Service,2011(9). |
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| Authors: | Bian Peng Zhao Yan Su Yuzhao |
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| Institution: | Bian Peng~(1,2) Zhao Yan~3 Su Yuzhao~(1,2) 1(National Science Library,Chinese Academy of Sciences,Beijing 100190,China) 2(Graduate University of Chinese Academy of Sciences,Beijing 100049,China) 3(Computer Science and Application Department,Zhengzhou Institute of Aeronautical Industry Management,Zhengzhou 450015,China) |
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| Abstract: | Based on the text clustering requirement from the embedded NSTL Recommending System,this paper researches on the BWP algorithm,and analyzes the shortage of the BWP.Then an improved algorithm is proposed to optimize the calculation of the distance within the single sample cluster.The improved algorithm enlarges the range of clusters number based on the BWP.Moreover,it changes the partial optimum into the whole optimum.At last,the test result shows it is effective and efficient. |
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| Keywords: | K-means cluster Cluster number Text clustering Recommending system |
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