Survey of Correlated Probabilistic Graph

Now a days probabilistic graph have more interest in the data mining community. After observation it is find that correlations may exist among adjacent edges in various probabilistic graphs. As one of the basic mining techniques, graph clustering is widely used in data analysis where a problem that has not been clearly defined, such as data compression, information retrieval, image segmentation, etc. Graph clustering is used to divide data into clusters according to their similarities, and a number of algorithms have been proposed for clustering graphs, such as the pKwik Cluster algorithm, spectral clustering, k-path clustering, etc. In this way, little research has been performed to develop efficient clustering algorithms for probabilistic graphs. But, it becomes more challenging to efficiently cluster probabilistic graphs when correlations are considered. In this paper, we define the problem of clustering correlated probabilistic graphs and its techniques which are used before and its problem. To solve the challenging problem two algorithms, namely the PEEDR and the CPGS clustering algorithm are defined for each of the proposed algorithms, and then also define some several pruning techniques to further improve their efficiency.