Missing data treatment method on cluster analysis

The missing data in household health survey was challenged for the researcher because of incomplete analysis. The statistical tool cluster analysis methodology implemented in the collected data of Sudan's household health survey in 2006. Current research specifically focuses on the data analysis as the objective is to deal with the missing values in cluster analysis. Two-Step Cluster Analysis is applied in which each participant is classified into one of the identified pattern and the optimal number of classes is determined using SPSS Statistics/IBM. However, the risk of over-fitting of the data must be considered because cluster analysis is a multivariable statistical technique. Any observation with missing data is excluded in the Cluster Analysis because like multi-variable statistical techniques. Therefore, before performing the cluster analysis, missing values will be imputed using multiple imputations (SPSS Statistics/IBM). The clustering results will be displayed in tables. The descriptive statistics and cluster frequencies will be produced for the final cluster model, while the information criterion table will display results for a range of cluster solutions.