Mining Dependent Frequent Serial Episodes from Uncertain Sequence Data

Publisher:
IEEE Computer Society Press
Publication Type:
Conference Proceeding
Citation:
Proceedings of the13th IEEE International Conference on Data Mining, 2013, pp. 1211 - 1216
Issue Date:
2013-01
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In this paper, we focus on the problem of mining Probabilistic Dependent Frequent Serial Episodes (P-DFSEs) from uncertain sequence data. By observing that the frequentness probability of an episode in an uncertain sequence is a Markov Chain imbeddable variable, we first propose an Embedded Markov Chain-based algorithm that efficiently computes the frequentness probability of an episode by projecting the probability space into a set of limited partitions. To further improve the computation efficiency, we devise an optimized approach that prunes candidate episodes early by estimating the upper bound of their frequentness probabilities.
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