Abstract
In this article we present a new string clustering algorithm and a statistical validation of discovered clusters. They are obtained by searching for common structures of strings and by grouping those sharing wide words. The application of a statistical test, estimating cluster homogeneity, allows to find automatically the number of classes. We apply our method to extract key-structures in 40 biological sequences of about 100 characters each in order to build clusters, and to find three original families again.
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© 1998 Springer-Verlag Berlin Heidelberg
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Sebban, M., Landraud-Lamole, A.M. (1998). String clustering and statistical validation of clusters. In: Mercer, R.E., Neufeld, E. (eds) Advances in Artificial Intelligence. Canadian AI 1998. Lecture Notes in Computer Science, vol 1418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64575-6_59
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DOI: https://doi.org/10.1007/3-540-64575-6_59
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