Abstract
This paper considers the simultaneous determination of data classification and linear regression models. The clustering criterion introduced in this paper includes two types of mixing parameters to make a balance between two objectives of clustering: the minimization of variances within clusters and the minimization of regression errors. The paper proposes an idea on dynamic determination of those parameters in the clustering process.
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© 1998 Springer-Verlag Berlin · Heidelberg
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Ryoke, M., Nakamori, Y., Tamura, H. (1998). Dynamic Determination of Mixing Parameters in Fuzzy Clustering. 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_15
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DOI: https://doi.org/10.1007/978-3-642-72253-0_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64641-9
Online ISBN: 978-3-642-72253-0
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