Abstract—It is found out that empathy plays a substantially essential role in the economic development and consequently in reducing poverty in the world. In this research our main focus is on human empathy to categorize the nations and compare their empathic level. The aim of this research is to find out the importance of empathic relationships among the countries for the mutual economic development of the countries. For this reason we need countries' statistical data related to economic and social development of the people of those countries, which is collected from the World Bank and United Nations web servers. We also collected data of financial aid given by the countries to other countries for the manifestation of empathic behavior. The empathic indicators are identified and are used for classification of countries using data mining techniques. The unsupervised classification technique "K-mean Clustering" is used to group the data that gave us five clusters of the 118 countries which are compared to one another for their empathic levels. The results indicate some useful and interesting analysis. As a conclusion, it is also suggested that in the future, the same kind of research can be carried out at the organization level.
Index Terms—empathy, data mining, clustering, classification, economic indicators
Cite: Muhammad Shahbaz, Irfan Mushtaq, and Aziz Guergachi, "Mining Empathic Indicators for Classification of Countries to Alleviate Poverty in the World," Vol. 6, No. 3, pp. 130-134, August, 2015. doi: 10.12720/jait.6.3.130-134
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