| 一个高效的KNN分类算法 |
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| 引用本文： | 张著英 黄玉龙 王翰虎. 一个高效的KNN分类算法[J]. 计算机科学, 2008, 35(3): 170-172 |
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| 作者姓名： | 张著英 黄玉龙 王翰虎 |
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| 作者单位： | 贵州大学计算机科学技术学院,贵阳,550025;贵州大学计算机科学技术学院,贵阳,550025;贵州大学计算机科学技术学院,贵阳,550025 |
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| 摘 要： | KNN算法是数据挖掘技术中比较常用的分类算法,由于其实现的简单性,在很多领域得到了广泛的应用.但是,当样本容量较大以及特征属性较多时,KNN算法分类的效率就将大大降低.本文将粗糙集理论应用到KNN算法中,实现属性约简,提出了一种新的KNN分类方法,解决了KNN算法分类效率低的缺点,从而可使KNN算法能够得到更广泛的应用.
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| 关 键 词： | 数据挖掘 KNN分类 粗糙集 属性约简 |
A New KNN Classification Approach |
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| ZHANG Zhu-Ying,HUANG Yu-Long,WANG Han-Hu (College of Computer Science Technology,Guizhou University,Guiyang. A New KNN Classification Approach[J]. Computer Science, 2008, 35(3): 170-172 |
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| Authors: | ZHANG Zhu-Ying HUANG Yu-Long WANG Han-Hu (College of Computer Science Technology Guizhou University Guiyang |
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| Abstract: | KNN algorithm has been widely used in many data mining areas due to its simplicity. When the samples become more and more large and characteristic attributes become more and more numerous,KNN algorithm becomes much lower. A new KNN algorithm based rough set theory is proposed in the paper,in order to improve the effectiveness. |
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| Keywords: | Data mining KNN classification Rough set Attributes induction |
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