Abstract
Emerging patterns are patterns of a great interest for characterizing classes. This task remains a challenge, especially with graph data. In this paper, we propose a method to mine the whole set of frequent emerging graph patterns, given a frequency threshold and an emergence threshold. Our results are achieved thanks to a change of the description of the initial problem so that we are able to design a process combining efficient algorithmic and data mining methods. Experiments on a real-world database composed of chemicals show the feasibility and the efficiency of our approach.
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Poezevara, G., Cuissart, B., Crémilleux, B. (2009). Discovering Emerging Graph Patterns from Chemicals. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds) Foundations of Intelligent Systems. ISMIS 2009. Lecture Notes in Computer Science(), vol 5722. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04125-9_8
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DOI: https://doi.org/10.1007/978-3-642-04125-9_8
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