Open AccessClustering and multiple imputation of missing data
Author(s) -
Elsiddig Elsadig Mohamed Koko,
Amin Ibrahim Adam Mohamed
Publication year - 2015
Publication title -
international journal of basic and applied sciences
Language(s) - English
Resource type - Journals
ISSN - 2227-5053
DOI - 10.14419/ijbas.v5i1.5470
Subject(s) - missing data , cluster analysis , imputation (statistics) , statistics , ibm , cluster (spacecraft) , sample (material) , population , computer science , data mining , mathematics , materials science , chromatography , programming language , nanotechnology , demography , sociology , chemistry
The present work 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. 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. Furthermore, goal of analysis is to reduce biases arising from the fact that non-respondents may be different from those who participate and to bring sample data up to the dimensions of the target population totals.