Abstract
This paper further extends the ‘kernel’-based approach to clustering proposed by E. Diday in early 70s. According to this approach, a cluster’s centroid can be represented by parameters of any analytical model, such as linear regression equation, built over the cluster. We address the problem of producing regression-wise clusters to be separated in the input variable space by building a hybrid clustering criterion that combines the regression-wise clustering criterion with the conventional centroid-based one.
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References
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© 2007 Springer-Verlag Berlin Heidelberg
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Stanforth, R., Kolossov, E., Mirkin, B. (2007). Hybrid k-Means: Combining Regression-Wise and Centroid-Based Criteria for QSAR. In: Brito, P., Cucumel, G., Bertrand, P., de Carvalho, F. (eds) Selected Contributions in Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73560-1_21
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DOI: https://doi.org/10.1007/978-3-540-73560-1_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73558-8
Online ISBN: 978-3-540-73560-1
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