A Novel Approaches on Clustering Algorithms And it’s Applications

Graph clustering algorithms are Random walk and minimum spanning tree algorithms. Random walk has been used to identify significant vertices in the graph that receive maximum flow while minimum spanning tree algorithm has been used to identify significant edges in the graph .We believe these two graph algorithms have useful applications in clustering, namely for identifying centroids and for identifying edges to merge or split clusters such that intra-cluster similarity is maximized while inter-cluster similarity is minimized. This paper investigates the graph algorithms, graphbased clustering algorithms, and their applications. graph algorithms and graph-based clustering algorithms, we propose novel variants of Star clustering algorithm that use different techniques for identifying centroids, and two novel graph-based clustering algorithms: MST-Sim and Ricochet. The variant graph algorithms and graph based clustering algorithms achieve higher performance in terms of effectiveness and efficiency for the applications of document clustering, k-member clustering, opinion mining, clustering for part-of-speech tagging. Keywords— Clustering algorithms, graph based clustering algorithms, ——————————  ——————————