nicholasnjiru/nicknjiru

Clustering and Visualizing the Status of Child Health in Kenya: A Data Mining Approach # Clustering and Visualizing the Status of Child Health in Kenya: A Data Mining Approach. The inauguration of the new constitution in Kenya led to the devolution of health care in the counties. It is against this backdrop that necessitated a need to develop a model of grouping these counties into natural groups with similar characteristics that can influence the child health for the purpose of health care planning and regulation. Little research has explored a methodology that can be used to create such groupings in Kenya. The purpose of this research was to develop and explore a methodology of Clustering and Visualizing the status of the child health in Kenya. In this research we proposed a new model that clustered the counties based on the UNICEF indicators of child health. The cluster analysis methodology employed to achieve this was by use of K-Means clustering algorithm. Both hierarchical and non-hierarchical clustering algorithms were used to build a consensus with the results of clusters obtained by K-Means. The number of clusters selected was based on heuristic, integrating a statistical-based measure of cluster fit. Using data from literature, the clustering methodology developed grouped the 47 counties into three distinctive clusters. These three clusters were made up of 10, 8 and 29 counties respectively. The study classified the clusters as well-off, most marginalized and moderately marginalized counties respectively. The methodology developed was objective, replicable and sustainable to create the clusters. It was developed in a theoretically sound principle and can be generalized across applications requiring clustering. An examination of several clustering algorithms revealed similar results. Keywords: Principal Component Analysis, K-Means, Clustering, Visualizing, Child Health Indicators, Data Mining, Dimensionality Reduction.