Abstract
The paper presents some new clustering algorithms which are based on fuzzy c-means. The algorithms can treat data with tolerance defined as hyper-rectangle. First, the tolerance is introduced into optimization problems of clustering. This is generalization of calculation errors or missing values. Next, the problems are solved and some algorithms are constructed based on the results. Finally, usefulness of the proposed algorithms are verified through numerical examples.
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Hasegawa, Y., Endo, Y., Hamasuna, Y., Miyamoto, S. (2007). Fuzzy c-Means for Data with Tolerance Defined as Hyper-Rectangle. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2007. Lecture Notes in Computer Science(), vol 4617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73729-2_23
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DOI: https://doi.org/10.1007/978-3-540-73729-2_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73728-5
Online ISBN: 978-3-540-73729-2
eBook Packages: Computer ScienceComputer Science (R0)