Abstract
Recently, semi-supervised clustering has been focused, e.g., Refs. [2–5]. The semi-supervised clustering algorithms improve clustering results by incorporating prior information with the unlabeled data. This paper proposes three new clustering algorithms with pairwise constraints by introducing non-metric term to objective functions of the well-known clustering algorithms. Moreover, its effectiveness is verified through some numerical examples.
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Endo, Y., Kinoshita, N., Iwakura, K., Hamasuna, Y. (2014). Hard and Fuzzy c-means Algorithms with Pairwise Constraints by Non-metric Terms. In: Torra, V., Narukawa, Y., Endo, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2014. Lecture Notes in Computer Science(), vol 8825. Springer, Cham. https://doi.org/10.1007/978-3-319-12054-6_13
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DOI: https://doi.org/10.1007/978-3-319-12054-6_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12053-9
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