Research on K Nearest Neighbor Non-parametric Regression Algorithm Based on KD-Tree and Clustering Analysis

Zheng-Wu Yuan, Yuan-Hui Wang
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引用次数: 10

Abstract

Regarding to the limitations of the existing K nearest neighbor non-parametric regression methods, spatial autocorrelation analysis is used to determine the state vector in this paper. In order to improve the speed of searching data, this paper uses the method of clipping samples to reduce data storage and split the sample quickly by KD-Tree. It also reduces the search volume of the nearest neighbor through the pruning principle of KD-Tree, gets the subset by proportional sampling in the KD-Tree subset, and runs K-Means clustering multiple times. Then the optimal K value is selected which can improve the forecast error of the uniform K value on the traditional non-parametric regression. The experimental results show that improved forecasting method is superior to the traditional method.
基于KD-Tree和聚类分析的K近邻非参数回归算法研究
针对现有K近邻非参数回归方法的局限性,本文采用空间自相关分析来确定状态向量。为了提高搜索数据的速度,本文采用样本裁剪的方法减少数据存储,并采用KD-Tree快速分割样本。利用KD-Tree的剪枝原理减少最近邻的搜索量,在KD-Tree子集中按比例采样得到子集,并多次运行K-Means聚类。然后选取最优K值,改善了传统非参数回归中均匀K值的预测误差。实验结果表明,改进的预测方法优于传统的预测方法。
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