A Dynamic Additive Fuzzy Clustering Model
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).
Keywords3-Way Data Dynamic MDS Clustering Model
Unable to display preview. Download preview PDF.
- Ambrosi, K. and Hansohm, J. (1987), “Ein dynamischer Ansatz zur Repräsentation von Objekten”, In Operations Research Proceedings 1986, Berlin: Springer-Verlag.Google Scholar
- Baba, Y. and Nakamura, Y. (1997), “Jikan Henka wo tomonau Syuseibun Bunsekihou (in Japanese)”, 11th Japanese Society of Computational Statistics, 82–85.Google Scholar
- Sato, M. and Sato, Y. (1994), “On a Multicriteria Fuzzy Clustering Method for 3-Way Data “, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2, 127–142.Google Scholar
- Sato, M. and Sato, Y. (1995), “On a General Fuzzy Additive Clustering Model”, International Journal of Intelligent Automation and Soft Computing, 1, No. 4, 439–448.Google Scholar
- Sato M. and Sato, Y. (1997), “Generalized Fuzzy Clustering Model for 3-way Data”, International Conference on Fuzzy Logic and its Applications, 132–137.Google Scholar
- Schweizer, B. and Sklar, A. (1983), Probabilistic Metric Space, North-Holland, New York.Google Scholar
- School Basic Survey, Ministry of Education in Japan, (1992).Google Scholar