Scientia Agricultura Sinica ›› 2012, Vol. 45 ›› Issue (21): 4534-4542.doi: 10.3864/j.issn.0578-1752.2012.21.023

• RESEARCH NOTES • Previous Articles    

A Dynamic Clustering Method with Missing Data

 XIAO  Jing, LUO  Ru-Jiu, SONG  Wen, TANG  Zai-Xiang, XU  Chen-Wu   

  1. 1.南通大学公共卫生学院流行病与卫生统计学教研室，江苏南通226019
    2.扬州大学江苏省作物遗传生理重点实验室，江苏扬州225009
    3.苏州大学医学部公共卫生学院流行病与卫生统计学教研室，江苏苏州215123
  • Received:2012-06-15 Online:2012-11-01 Published:2012-09-18

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Abstract: 【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.

Key words: cluster analysis, missing data, posterior probability, maximum likelihood estimation

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