Research theme for competitive and other funds (3):
2013 - 2016 Development of integrated algorithm of fuzzy clustering with entropy maximization and annealing
2007 - 2009 Entropy based statistical mechanical fuzzy clustering method and its visualization
情報統計力学とソフトコンピューティングのファジィクラスタリングへの応用
Papers (57):
Kazuhiro Tamada, Makoto Yasuda. Determination of number of clusters for fuzzy c-means maximized with Tsallis entropy. Advances in Intelligent Systems and Computing, Springer. 2021. 1348. 446-456
yasuda makoto. Determination of multiple q values for Tsallis-entropy-maximized-FCM. Advances in Intelligent Systems and Computing, Springer. 2020. 1074. 771-780
yasuda makoto. On utilization of Canopy clustering method for determination of q-parameter for Tsallis-DAFCM. Proc. of the 2018 Int. Conf. on Natural Computation, Fuzzy Systems and Knowledge Discovery. 2018
Determination of number of clusters for fuzzy c-means maximized with Tsallis entropy
(The 2020 16th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery 2020)
Determination of multiple q values for Tsallis-entropy-maximized-FCM
(The 2019 15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery 2019)
On utilization of Canopy clustering method for determination of q-parameter for Tsallis-entropy-maximized-FCM
(Proc. of the 2018 Int. Conf. on Natural Computation, Fuzzy Systems and Knowledge Discovery 2018)
On utilization of k-means for determination of q-parameter for Tsallis-entropy-maximized-FCM
(2017 Int. Conf. on Natural Computation, Fuzzy Systems and Knowledge Discovery 2017)
Approximate determination of q-parameter for FCM with Tsallis entropy maximization
(Joint 8th Int. Conf. on Soft Computing and 17th Int. Symp. on Advanced Intelligent Systems 2016)