A Dynamic Clustering Method with Missing Data
Chenwu Xu · Zhongguo nongye Kexue · 2012
【Objective】 The aim of the study is to investigate a clustering method for clustering the data with missing values in practice research.【Method】The paper introduces a maximum likelihood-based dynamic clustering method,which could configure a complete data set through the maximum likelihood estimation for the missing by statistics of the others.The parameters of missing data and different clusters are estimated by the maximum likelihood method implemented via expectation-maximization(EM) algorithm and the objects are classified by the Bayesian posterior probability.【Result】 The results of simulation studies show that the proposed method not only has fast convergence speed but also accurately cluster the data with missing values.【Conclusion】The proposed method was further validated by Fisher's Iris dataset.The result indicated that the proposed method had a significant advantage on clustering accuracy compared to the delete missing data arithmetic and it is similar to complete data clustering algorithm.