Efficient clustering of big data using graph method

The data mining process is to extract the information from a large data set and transform the extracted data into an understandable structure for further use. Clustering is a main task of exploratory data analysis and data mining applications. Clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters). Power Iteration Clustering (PIC) algorithm is recently identified algorithm which helps to create a good quality of cluster. PIC is simple and scalable graph clustering technique. In PIC the embedding is an approximation to a eigen value-weighted linear combination of all the eigenvectors of an normalized similarity matrix. This embedding turns out to be very effective for clustering. This work shows that PIC consistently outperformed when the process is parallelized and it achieves fault tolerance by branch and bound algorithm. Parallelization minimizes computation cost, this algorithm works on all lower end commodity computers.