Correlated Probabilistic Graph with Clustering - Sawant Ashlesha, G (International Journal of Computer Sciences and Engineering, 2025)
Recently, probabilistic graph have more interest in the data mining. After some result it is found that correlations may exist among adjacent edges in various probabilistic graphs. As one of the basic mining techniques, graph clustering is widely used. Different Clustering methods are used. But, when correlations are considered, it becomes more challenging to efficiently cluster probabilistic graphs. Here, we define the problem of clustering correlated probabilistic graphs and its techniques. To solve the challenging problem the PEEDR and the DPTC clustering algorithm are defined for each of the proposed algorithms, with some several pruning techniques and Different Similarity measures.