Abstract
A well-known limitation of a lot of data mining methods is the huge number of patterns which are discovered: these large outputs hamper the individual and global analysis performed by the end-users of data. That is why discovering patterns of higher level is an active research field. In this paper, we investigate the relationship between local constraint-based mining and constraint satisfaction problems and we propose an approach to model and mine patterns combining several local patterns, i.e., patterns defined by n-ary constraints. The user specifies a set of n-ary constraints and a constraint solver generates the whole set of solutions. Our approach takes benefit from the recent progress on mining local patterns by pushing with a solver on local patterns all local constraints which can be inferred from the n-ary ones. This approach enables us to model in a flexible way any set of constraints combining several local patterns. Experiments show the feasibility of our approach.
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Khiari, M., Boizumault, P., Crémilleux, B. (2010). Combining CSP and Constraint-Based Mining for Pattern Discovery. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2010. ICCSA 2010. Lecture Notes in Computer Science, vol 6017. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12165-4_35
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DOI: https://doi.org/10.1007/978-3-642-12165-4_35
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