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Effective and Efficient Clustering Methods for Correlated Probabilistic Graphs
May 2014 (vol. 26 no. 5)
pp. 1-1
Ge Yu, College of Information Science and Engineering, Northeastern University, Shenyang, China
Chunpeng Gao, College of Information Science and Engineering, Northeastern University, Shenyang, China
Gao Cong, , Nanyang Technological University, Singapore
Yu Gu, College of Information Science and Engineering, Northeastern University, Shenyang, China
Recently, probabilistic graphs have attracted significant interests of the data mining community. It is observed that correlations may exist among adjacent edges in various probabilistic graphs. As one of the basic mining techniques, graph clustering is widely used in exploratory data analysis, such as data compression, information retrieval, image segmentation, etc. Graph clustering aims to divide data into clusters according to their similarities, and a number of algorithms have been proposed for clustering graphs, such as the pKwikCluster algorithm, spectral clustering, k-path clustering, etc. However, little research has been performed to develop efficient clustering algorithms for probabilistic graphs. Particularly, 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. To solve the challenging problem, we propose two algorithms, namely the $PEEDR$ and the $CPGS$ clustering algorithm. For each of the proposed algorithms, we develop several pruning techniques to further improve their efficiency. We evaluate the effectiveness and efficiency of our algorithms and pruning methods through comprehensive experiments.
Index Terms:
Probabilistic logic,Clustering algorithms,Correlation,Algorithm design and analysis,Probability,Joints,Data mining,Data mining,Clustering,classification,and association rules
Citation:
Ge Yu, Chunpeng Gao, Gao Cong, Yu Gu, "Effective and Efficient Clustering Methods for Correlated Probabilistic Graphs," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 5, pp. 1-1, May 2014, doi:10.1109/TKDE.2013.123
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