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