Abstract
According to the idea of the predictive power of a class in a classification (Gilmour 1951), our aim is to build maximal predictive classifications (Gower 1974) of objects in a sequence, that respect the total order defined by this sequence. We propose a new dynamic programming algorithm able to discover a maximal predictive partition and which complexity is linear with the length of the sequence and with the number of possible predictors. This algorithm accepts vast range of predictor shapes and may be used for numerous possible applications. We present an experiment of this clustering algorithm on biological sequences.
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© 1998 Springer-Verlag Berlin · Heidelberg
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Guéguen, L., Vignes, R., Lebbe, J. (1998). Maximal predictive clustering with order constraint: a linear and optimal algorithm. In: Rizzi, A., Vichi, M., Bock, HH. (eds) Advances in Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72253-0_21
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DOI: https://doi.org/10.1007/978-3-642-72253-0_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64641-9
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