Abstract
Frequent subgraph mining is a popular data mining task, which consists of finding all subgraphs that appear in at least minsup graphs of a graph database. An important limitation of traditional frequent subgraph mining algorithms is that the minsup parameter is hard to set. If set too high, few patterns are found and useful information may be missed. But if set too low, runtimes can become very long and a huge number of patterns may be found. Finding an appropriate minsup value to find just enough patterns can thus be very time-consuming. This paper addresses this limitation by proposing an efficient algorithm named TKG to find the top-k frequent subgraphs, where the only parameter is k, the number of patterns to be found. The algorithm utilizes a dynamic search procedure to always explore the most promising patterns first. An extensive experimental evaluation shows that TKG has excellent performance and that it provides a valuable alternative to traditional frequent subgraph mining algorithms.
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The work presented in this paper has been partly funded by the National Science Foundation of China.
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Fournier-Viger, P., Cheng, C., Lin, J.CW., Yun, U., Kiran, R.U. (2019). TKG: Efficient Mining of Top-K Frequent Subgraphs. In: Madria, S., Fournier-Viger, P., Chaudhary, S., Reddy, P. (eds) Big Data Analytics. BDA 2019. Lecture Notes in Computer Science(), vol 11932. Springer, Cham. https://doi.org/10.1007/978-3-030-37188-3_13
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