Abstract
This paper presents a dynamic clustering model in which clusters are constructed in order to find the features of the dynamical change.
If the similarity between the objects is observed depending on time or parameters which are satisfying the total order relation, then it is important to capture the change in the results of clustering according to the change in time. In this paper, we construct a model which can represent dynamically changing clusters by introducing the concepts of conventional dynamic MDS (Ambrosi, K. and Hansohm, J., 1987) or dynamic PCA (Baba, Y. and Nakamura, Y., 1997) into the additive clustering model (Sato, M. and Sato, Y., 1995).
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References
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© 1998 Springer-Verlag Berlin · Heidelberg
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Sato-Ilic, M., Sato, Y. (1998). A Dynamic Additive Fuzzy Clustering Model. 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_16
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DOI: https://doi.org/10.1007/978-3-642-72253-0_16
Publisher Name: Springer, Berlin, Heidelberg
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