微型电脑应用2018，Vol.34Issue(5)：50-52,75,4.
基于高斯混合模型的最大期望聚类算法研究
Research on Maximum Expected Clustering Algorithm Based on Gaussian Mixture Model
何庆 1易娜 1汪新勇 1江立斌1
作者信息
- 1. 中国移动通信集团广东公司,广州510623
- 折叠
摘要
Abstract
Clustering analysis uses data mining and machine learning techniques to search data,the traditional clustering analysis method uses serial method to process data,it has high requirements on computer memory and CPU,it is not suitable for mass data analysis.Gaussian mixture model uses the propability density function to accurately quantify the data,distribute the data to the various mixed components,in order to find the refinement of the data,simplify the data analysis steps.At the same time,the Gaussian mixture model is used to analyze the data,the maximum expectation (EM) algorithm is mainly used to evaluate the parameters to further,they improve the data analysis efficiency.This paper mainly introduces the principle of maximum expectation clustering algorithm and optimizes the Gaussian mixture model,the purpose is to make it adapt to a wider range.At the same time,combined with the case data on the Hadoop platform,the algorithm is analyzed and the probability density of the sample analysis is displayed by using the visual graph.The study finds that the maximum expectation clustering algorithm is suitable for mass data analysis,which is calculated by Gaussian mixture model,greatly improving the efficiency of clustering analysis.关键词
高斯混合模型/最大期望/聚类算法Key words
Gaussian mixture model/Maximum expectation/Clustering algorithms分类
矿业与冶金引用本文复制引用
何庆,易娜,汪新勇,江立斌..基于高斯混合模型的最大期望聚类算法研究[J].微型电脑应用,2018,34(5):50-52,75,4.
