Abstract
Discovering patterns in graphs is a well-studied field of data mining. While a lot of work has already gone into finding structural patterns in graph datasets, we focus on relaxing the structural requirements in order to find items that often occur near each other in the input graph. By doing this, we significantly reduce the search space and simplify the output. We look for itemsets that are both frequent and cohesive, which enables us to use the anti-monotonicity property of the frequency measure to speed up our algorithm. We experimentally demonstrate that our method can handle larger and more complex datasets than the existing methods that either run out of memory or take too long.
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Hendrickx, T., Cule, B., Goethals, B. (2014). Mining Cohesive Itemsets in Graphs. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds) Discovery Science. DS 2014. Lecture Notes in Computer Science(), vol 8777. Springer, Cham. https://doi.org/10.1007/978-3-319-11812-3_10
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DOI: https://doi.org/10.1007/978-3-319-11812-3_10
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