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Human Capital or HC plays a significant role in the field of economic growth. Advancement on human capital and knowledge-based economy are of core importance for developing countries. Active representatives of capital accumulation are the people and utilizing natural resources for contributing to the socioeconomic growth of the country. Evaluation of HC helps organizations to concern about their present position taking Human Capital Management into account. The core goal of this work is to measure performance of each organization individually based on criteria and rating the individual depending on the measurements. The technique used in this paper is based on the integration of "AHP" and "TOPSIS" as "AHP-TOPSIS" Hybrid method. The Analytic Hierarchy Process determines criteria's weight and significance of the indicators or alternatives. The final ranking of HC is done by TOPSIS method considering the importance of the indicators. The proposed MCDM approach is effective, compatible and reliable considering the goal of the study.

Clustering is the methodology to separate similar objects of data set in one cluster and dissimilar objects of data set in another cluster. K means and K medoids are most widely used Clustering algorithms for selecting group of objects for data sets. k means clustering has less time complexity than k medoids method, but k means clustering method suffers from extreme values. So, we have focused our view to k medoids clustering method. Conventional k-medoids clustering algorithm suffers from many limitations. We have done analysis on these limitations such as the problem of finding natural clusters, the dependency of output on the order of input data. In this paper we have proposed a new algorithm named Grid Multidimensional K medoids which is designed to overcome the above limitations and provide a faster clustering than K medoids..

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