Abstract
In this paper, first we introduce a bipartite episode of the form A ↦B for two sets A and B of events, which means that every event of A is followed by every event of B. Then, we present an algorithm that finds all frequent bipartite episodes from an input sequence without duplication in O(|Σ| ·N) time per an episode and in O(|Σ|2 n) space, where Σ is an alphabet, N is total input size of \(\mathcal S\), and n is the length of S. Finally, we give experimental results on artificial and real sequences to evaluate the efficiency of the algorithm.
This work is partially supported by Grand-in-Aid for JSPS Fellows (20·3406).
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Katoh, T., Arimura, H., Hirata, K. (2009). Mining Frequent Bipartite Episode from Event Sequences. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds) Discovery Science. DS 2009. Lecture Notes in Computer Science(), vol 5808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04747-3_13
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DOI: https://doi.org/10.1007/978-3-642-04747-3_13
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