الفهرس 
Title : A generalized mathematical programming model for parsimonious robust clusterwise linear regression
ContentsOnly 14 pages are availabe for public view
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Abstract
In the last decades, clusterwise regression has received considerable attention. Existence of outliers in the dataset or usage of unimportant explanatory variables in fitting the regression lines leads to inaccurate clustering and regression lines. In this thesis, a mixed integer goal programming model is proposed to obtain a parsimonious robust clusterwise linear regression. The proposed model also can determine the number of homogenous clusters in a dataset and detect the outliers. The proposed model is applied to some datasets. Also, the performance of the model is evaluated using a simulation study. Finally, the proposed model is applied to the Egypt 2016 Enterprise Survey data. This application aims to discover the factors that affect sales performance of the Egyptian companies and give some suggestions for improving the existing conditions of the companies