基于PCA和k - means++的数据分类算法

Ruirui Huang, Qianshuai Cheng, Zhengquan Chen
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引用次数: 0

摘要

利用主成分分析(PCA)和k - memeans ++算法对中国31个省市城镇家庭某年平均消费支出的8个主要变量进行分类分析,从而更好地构建各地区合理消费水平。本文提出了一种基于PCA和改进k -means++的数据分类方法。该方法采用主成分分析法提取消费者支出的8个变量特征,并采用k -means++算法对数据进行分类。实验结果表明,该方法降低了变量数据特征维数和k-means++算法对初始分类重心的依赖,提高了数据分类结果的准确性和稳定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Algorithm of Data Classification Based on PCA and K-Means++
Principal component analysis (PCA) and K-means++ algorithm are used to classify and analyze eight main variables of the average annual consumption expenditure of urban households in 31 Provinces and cities in China in a given year, so as to better build the reasonable consumption level of each area. In this paper, a data classification method based on PCA and improved K-means++ is proposed. In this method, PCA was used to extract eight variable characteristics of consumer expenditure, and K-means++ algorithm was used to classify the data. The experimental results show that this method reduces the dependence of the characteristic dimension of variable data and the k-means++ algorithm on the initial classification center of gravity, and improves the accuracy and stability of data classification results.
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