A hybrid clustering technique to propose the countries for help international

Abstract

HELP International is a charitable nongovernmental organization (NGO) that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during the time of disasters and natural calamities. HELP International has been able to raise around $ 10 million. Therefore, the head of the NGO needs to decide how to use this money strategically and effectively. Hence, the Head requires to make a decision for choosing the countries that are in the direst need of aid. This paper uses a hybrid clustering technique to suggest countries based upon socio-economic and health factors that determine the overall development of the country. The hybrid technique applies K-MEANS clustering and Farthest-First algorithm for clustering the countries. Both techniques are part of unsupervised learning tasks, which group data into multiple clusters. The hybrid technique proposes the countries that are most in need of help to HELP International. Moreover, it helps the head of the NGO in making the decision of choosing the countries that are in the direst need of aid and increase the number of countries without risk according to overall development factors by clustering techniques to HELP International socio-economic and health factors clustering.