Abstract
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.
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© 2013 Springer-Verlag Berlin Heidelberg
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Shang, L., Jian, Y. (2013). Mining Top-K Frequent Correlated Subgraph Pairs in Graph Databases. In: Abraham, A., Thampi, S. (eds) Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32063-7_1
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DOI: https://doi.org/10.1007/978-3-642-32063-7_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32062-0
Online ISBN: 978-3-642-32063-7
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