الفهرس 
A mathematical programming approach to cluster analysis for mixed data
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Abstract
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields. Most of the existing clustering approaches is applicable for purely numeric or categorical data only, but in the real life it is more common to find mixed data which consist of numeric and categorical data. This thesis presents a mathematical programming model that is capable of clustering mixed data. A nonlinear binary model is suggested for clustering mixed data by minimizing the distance between observations within groups. A new dissimilarity function is proposed to deal with mixed numeric and categorical data, simulation study to assess the performance of the proposed model. Finally an application is introduced o cluster Egyptian households to poor and non-poor according to the variables of the multidimensional poverty index