Abstract
A large number of clustering algorithms have been proposed to handle target data and deal with various real-world problems such as uncertain data mining, semi-supervised learning and so on. We focus above two topics and introduce two concepts to construct significant clustering algorithms. We propose tolerance and penalty-vector concepts for handling uncertain data. We also propose clusterwise tolerance concept for semi-supervised learning. These concepts are quite similar approach in the viewpoint of handling objects to be flexible to each clustering topics. We construct two clustering algorithms FCMT and FCMQ for handling uncertain data. We also construct two clustering algorithms FCMCT and SSFCMCT for semi-supervised learning. We consider that those concepts have a potential to resolve conventional and brand new clustering topics in various ways.
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Hamasuna, Y., Endo, Y. (2017). On Some Clustering Algorithms Based on Tolerance. In: Torra, V., Dahlbom, A., Narukawa, Y. (eds) Fuzzy Sets, Rough Sets, Multisets and Clustering. Studies in Computational Intelligence, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-319-47557-8_6
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DOI: https://doi.org/10.1007/978-3-319-47557-8_6
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