Abstract
Constraint-based pattern discovery is at the core of numerous data mining tasks. Patterns are extracted with respect to a given set of constraints (frequency, closedness, size, etc). In practice, many constraints require threshold values whose choice is often arbitrary. This difficulty is even harder when several thresholds are required and have to be combined. Moreover, patterns barely missing a threshold will not be extracted even if they may be relevant. In this paper, by using Constraint Programming we propose a method to integrate soft threshold constraints into the pattern discovery process. We show the relevance and the efficiency of our approach through a case study in chemoinformatics for discovering toxicophores.
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Ugarte, W., Boizumault, P., Loudni, S., Crémilleux, B. (2012). Soft Threshold Constraints for Pattern Mining. In: Ganascia, JG., Lenca, P., Petit, JM. (eds) Discovery Science. DS 2012. Lecture Notes in Computer Science(), vol 7569. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33492-4_25
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DOI: https://doi.org/10.1007/978-3-642-33492-4_25
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