Abstract
This paper studies sample applications of skeletal algorithm to process mining and automata discovery. The basic idea behind the skeletal algorithm is to express a problem in terms of congruences on a structure, build an initial set of congruences, and improve it by taking limited unions/intersections, until a suitable condition is reached. Skeletal algorithms naturally arise in the context of process minig and automata discovery, where the skeleton is the “free” structure on initial data and a congruence corresponds to similarities in data. In such a context, skeletal algorithms come equipped with fitness functions measuring the complexity of a model. We examine two fitness functions for our sample problem — one based on Minimum Description Length Principle, and the other based on Bayesian Interpretation.
This work has been partially supported by Polish National Science Center, project DEC-2011/01/N/ST6/02752.
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Przybylek, M.R. (2013). Skeletal Algorithms in Process Mining. In: Madani, K., Dourado, A., Rosa, A., Filipe, J. (eds) Computational Intelligence. IJCCI 2011. Studies in Computational Intelligence, vol 465. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35638-4_9
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