| 一种聚类模式下基于密度的改进KNN算法 |
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| 引用本文： | 刘海峰,庞秀梅,张学仁.一种聚类模式下基于密度的改进KNN算法[J].微电子学与计算机,2011,28(7). |
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| 作者姓名： | 刘海峰 庞秀梅 张学仁 |
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| 作者单位： | 解放军理工大学,理学院,江苏,南京,210007 |
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| 基金项目： | 国家自然科学基金项目(71071161) |
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| 摘 要： | KNN是基于实例的算法,训练样本的数量影响KNN的分类性能.合理的样本剪裁可以提高分类器的效率.提出了一种聚类条件下基于密度的KNN改进模型.首先使用聚类方法对训练集进行基于类别的选择,裁剪边缘样本以减少噪音;再基于类别密度对样本进行加权,改善k近邻选择时大类别、高密度训练样本的占优现象.试验结果表明,本文提出的改进KNN分类算法提高了KNN的分类效率.
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| 关 键 词： | k-最近邻 文本聚类 样本密度 文本分类 |
An Improved Density-Based KNN Algorithm under Clustering |
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| LIU Hai-feng,PANG Xiu-mei,ZHANG Xue-ren.An Improved Density-Based KNN Algorithm under Clustering[J].Microelectronics & Computer,2011,28(7). |
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| Authors: | LIU Hai-feng PANG Xiu-mei ZHANG Xue-ren |
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| Affiliation: | LIU Hai-feng,PANG Xiu-mei,ZHANG Xue-ren(Institute of Sciences,PLA University of Science and Technology,Nanjing 210007,China) |
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| Abstract: | KNN is one of the arithmetic which based on the instance.The number of training samples influence on the classification performance of KNN.Reasonable sample cut can improve the efficiency of classification.This paper proposes an improved density-based KNN model under the clustering conditions.Firstly,basing on the types,we used the clustering method to choice the samples in order to reduce the noise samples.Then we weighted samples basing on sort density and overcame the defect that the big class,high densi... |
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| Keywords: | KNN text clustering sample density text categorization |
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