dc.contributor.author | Njiru, Nicholas M | |
dc.date.accessioned | 2016-11-18T06:56:32Z | |
dc.date.available | 2016-11-18T06:56:32Z | |
dc.date.issued | 2015-12 | |
dc.identifier.uri | http://hdl.handle.net/11295/97524 | |
dc.description.abstract | 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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | University of Nairobi | en_US |
dc.subject | Principal Component Analysis, K-Means, Clustering, Visualizing, Child Health Indicators, Data Mining, Dimensionality Reduction. | en_US |
dc.title | Clustering and visualizing the status of child health in Kenya: a data mining approach | en_US |
dc.type | Thesis | en_US |