A Survey on Efficient Clustering Methods with Effective Pruning Techniques for Probabilistic Graphs

This paper provides a survey on K-NN queries, DCR query, agglomerative complete linkage clustering and Extension of edit-distance-based definition graph algorithm and solving decision problems under uncertainty. This existing system give an beginning to Graph agglomeration aims to divide information into clusters per their similarities, and variety of algorithms are planned for agglomeration graphs, the pKwik Cluster algorithm, spectral agglomeration, k-path agglomeration, etc. However, very little analysis has been performed to develop efficient agglomeration algorithms for probabilistic graphs. Finally, The Graph algorithm to understand how to mining can be done efficiently. This survey introduced to design algorithm for searching and to evaluate the algorithm throw analysis.