Data analysis & classification methodology for knowledge discovery in big data

Big data applications are developed and being explored by the computer science organization, which is classified and accepted by huge data sets collected from sensor networks, online networks, medical agencies, etc. To deal with the difficulty in analysis of data, we conduct research on the novel algorithms for data mining and knowledge discovery through network property. At first, we introduce necessary data analysis techniques likes decision tree technique using KEEL tool, and finally, we analyze and classify the structure and graphical pattern of the data, with the help of machine learning methodology and graph theory. Eventually, our tailored method is finalized with decision tree for validation purpose. The simulation results of our approach on different databases show the feasibility and effectiveness of our proposed framework.