INSTITUTION SYSTEM ANALYSIS BY USING SIMILARITY BASED CLUSTERING ON SOCIAL NETWORK ACCESS

Abstract

The SNA (social network analysis), branch of difficult systems can be utilized in the construction of multi-agent systems.
This paper proposes a Institutional system using SNA network analysis which can assist in modeling multi-agent systems, when
addressing similarities and differences among the two theories. We fabricated a model of multi-agent systems for determination
of errands through the development of groups of agents that are shaped on the social's premise system built up between agents.
Agents make utilization of execution pointers to survey when ought to change their social network to expand the support in
groups. There are two issues on that we tend to focus during this paper. the primary one is to seek out the intrinsic institution
network structure and other is to check funding policies within the previous years and search for the optimum policy. So, to
overcome on this issue, we proposed the similarity based clustering for categorize the institution dataset, and this procedure
utilizes a multi-agent system, it is constructed on agent interactions. As well as searching for feasibleassociation between
student performance and funding policies. After cluster the datasets, then it’s stored into Databases based on highest similarity.
Also in this system mainly focuses to user (i.e. student, teacher, others) etc. retrieves the top most order institution from the DB.
At last, Institution will know the quality of their colleges, when compared to other Institution of highest similarity of Institution.