Abstract
We propose a definition for frequent approximate patterns in order to model important subgraphs in a graph database with incomplete or inaccurate information. By our definition, frequent approximate patterns possess three main properties: possible absence of exact match, maximal representation, and the Apriori Property. Since approximation increases the number of frequent patterns, we present a novel randomized algorithm (called RAM) using feature retrieval. A large number of real and synthetic data sets are used to demonstrate the effectiveness and efficiency of the frequent approximate graph pattern model and the RAM algorithm.
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Zhang, S., Yang, J. (2008). RAM: Randomized Approximate Graph Mining. In: Ludäscher, B., Mamoulis, N. (eds) Scientific and Statistical Database Management. SSDBM 2008. Lecture Notes in Computer Science, vol 5069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69497-7_14
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DOI: https://doi.org/10.1007/978-3-540-69497-7_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69476-2
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