Mining Top-K Frequent Correlated Subgraph Pairs in Graph Databases
In this paper, a novel algorithm called KFCP(top K Frequent Correlated subgraph Pairs mining) was proposed to discover top-k frequent correlated subgraph pairs from graph databases, the algorithm was composed of two steps: co-occurrence frequency matrix construction and top-k frequent correlated subgraph pairs extraction.We use matrix to represent the frequency of all subgraph pairs and compute their Pearson’s correlation coefficient, then create a sorted list of subgraph pairs based on the absolute value of correlation coefficient. KFCP can find both positive and negative correlations without generating any candidate sets; the effectiveness of KFCP is assessed through our experiments with real-world datasets.
Unable to display preview. Download preview PDF.
- 1.Morishita, S., Sese, J.: Traversing itemset lattice with statistical metric pruning. In: Proc. of PODS, pp. 226–236 (2000)Google Scholar
- 3.Xiong, H., Shekhar, S., Tan, P., Kumar, V.: Exploiting a support-based upper bound of Pearson’s correlation coefficient for efficiently identifying strongly correlated pairs. In: Proc. ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining, pp. 334–343. ACM Press (2004)Google Scholar
- 4.Xiong, H., Brodie, M., Ma, S.: Top-cop: Mining top-k strongly correlated pairs in large databases. In: ICDM, pp. 1162–1166 (2006)Google Scholar
- 5.Pan, J.Y., Yang, H.J., Faloutsos, C., Duygulu, P.: Automatic multimedia cross-modal correlation discovery. In: Proc. of KDD, pp. 653–658 (2004)Google Scholar
- 6.Sakurai, Y., Papadimitriou, S., Faloutsos, C.: Braid: Stream mining through group lag correlations. In: SIGMOD Conference, pp. 599–610 (2005)Google Scholar
- 7.Ke, Y., Cheng, J., Ng, W.: Correlation search in graph databases. In: Proc. of KDD, pp. 390–399 (2007)Google Scholar
- 8.Ke, Y., Cheng, J., Yu, J.X.: Efficient Discovery of Frequent Correlated Subgraph Pairs. In: Proc. of ICDM, pp. 239–248 (2009)Google Scholar