An Adaptive Approach for Clustering Incomplete Data Sets

The health inequality structure needs to be revealed in order to allow policy makers both globally and locally to find out how health can be promoted using limited resources available to the poor countries of the world. Therefore, the aim of this book is to produce a new algorithm for health inequality analysis that overcomes limitations of the current algorithms used. This algorithm can generally be used to analyse cluster structure within small, sparse and incomplete data sets. The proposed algorithm automatically determines the number of clusters based on well-defined theoretical bases, and also it quantifies relative contribution of individual health indicators to the creation of health inequality structure. This book is intended to be useful for numerical data analysts especially health inequality analysts. Also, it is useful for any one interested in revealing cluster structure within an incomplete data set.

Autorentext

Ahmed R. Abas, PhD: studied Computer Science at Exeter University, UK. Assistant Professor at Zagazig University, Egypt.