Analysis of amino acids network based on graph mining

Applications of graph mining have proliferated across the research spectrum in recent years. Mining data to retrieve information is a big deal as data are unstructured and huge in size, volume and in different data-types as internet is available to everyone and anywhere. Therefore data is so rapidly increased and for that point this mining concept came. In graph mining, analysis of graph base data is considered. In different research fields use graph base mining as it give quick and efficient result of large datasets. Here we consider biological data which are very complex to describe and analyse to extract useful information, so now researchers use computational tools to mine the large datasets, graphs are the most efficiently used. We consider amino acid network to do graph mining and extract some useful patterns from the network. Amino Acid Networks (AANs) are undirected graphs where amino acids are act like vertices and their relationships connect two vertices in protein structures. Every amino acid exhibits different physico-chemical properties. The shift in R groups affects various characteristics of the amino acids. The shift in R groups affects the various characteristics of the amino acids. In this paper we have construct a graph of amino acids based on property similarity and discussed different measures of centrality. We have also investigated the correlation coefficients between different measures of centrality.