Abstract
In this paper, we present a method to model frequent patterns and their interaction relationship in sequences based on complex network. First, an algorithm NOSEM is proposed to find non-overlapping pattern instances in sequence. Then, we give a new way to construct state model of sequence formed by non-overlapping patterns, namely pattern state model. The proposed pattern state model of sequence is a graph-like model. We discover that the graph formed by non-overlapping frequent patterns and their interaction relationship is a complex network. Experiments on real-world datasets and synthetic datasets show that the pattern sate models formed by the frequent patterns of sequences in almost all the domain are complex network. However, models in different domains have distinct power-law values, which are used to classify various types of sequence.
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Wan, L., Shu, K., Guo, Y. (2012). Sequences Modeling and Analysis Based on Complex Network. In: Zhao, M., Sha, J. (eds) Communications and Information Processing. Communications in Computer and Information Science, vol 288. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31965-5_29
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DOI: https://doi.org/10.1007/978-3-642-31965-5_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31964-8
Online ISBN: 978-3-642-31965-5
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