Abstract—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
data/process mining, where the skeleton is the “free” structure
on initial data and congruence corresponds to similarities in
data. In this paper we study skeletal algorithms applied to
sequential pattern mining and compare their performance with
real models, Markov chains and models based on Shannon
entropy.
Index Terms—Evolutionary algorithms, pattern mining,
process mining, language recognition, skeletal algorithms.
Michal R. Przybylek is with the University of Warsaw, Poland (e-mail:
mrp@mimuw.edu.pl).
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Cite: Michal R. Przybylek, "Skeletal Algorithms: Sequential Pattern Mining," International Journal of Computer Theory and Engineering vol. 7, no. 2, pp. 132-138, 2015.
