Research and Implementation of EM Clustering Algorithm Based on Latent Variable Mining

Clustering analysis is one of the hot research fields in data mining. EM algorithm is an effective method to realize maximum likelihood estimation, which is mainly used for parameter estimation of incomplete data. It greatly simplifies the likelihood function equation by assuming the existence of latent variable, while maximum likelihood estimation is a commonly used parameter estimation method, and the EM algorithm makes its application more extensive. This paper takes clustering algorithm as the main research object, introduces the basic idea of maximum likelihood estimation, describes the basic theory of EM algorithm, and realizes EM algorithm. The experimental results show that compared with the traditional clustering algorithm, the EM algorithm has better convergence and clustering ability.