Mika Sato-Ilic

Chee Peng Lim, Canicious Abeynayake, Mika Sato-Ilic, Lakhmi C. Jain: Special issue: Computational intelligence models for image processing and information reasoning. Journal of Intelligent and Fuzzy Systems 24(2): 199-200 (2013)

Tomoyuki Kuwata, Mika Sato-Ilic, Lakhmi C. Jain: A learning based self-organized additive fuzzy clustering method and its application for EEG data. KES Journal 16(1): 69-78 (2012)

Tetsuaki Kawase, Atsuko Maki, Akitake Kanno, Nobukazu Nakasato, Mika Sato, Toshimitsu Kobayashi: Contralateral white noise attenuates 40-Hz auditory steady-state fields but not N100m in auditory evoked fields. NeuroImage 59(2): 1037-1042 (2012)

Mika Sato-Ilic: Preface to Part III Adaptive Big Data Analytics. Procedia CS 12: 211 (2012)

Ryunosuke Chiba, Mika Sato-Ilic: Analysis of Web Survey Data based on Similarity of Fuzzy Clusters. Procedia CS 12: 224-229 (2012)

Mika Sato-Ilic: On Fuzzy Clustering based Correlation. Procedia CS 12: 230-235 (2012)

Mika Sato-Ilic: Structural classification based correlation and its application to principal component analysis for high-dimension low-sample size data. FUZZ-IEEE 2012: 1-8

Mika Sato-Ilic: Symbolic Clustering with Interval-Valued Data. Procedia CS 6: 358-363 (2011)

Mika Sato-Ilic: Generalized operators and its application to a nonlinear fuzzy clustering model. CIBCB 2011: 69-75

Mika Sato-Ilic: An adaptive cluster-target covariance based principal component analysis for interval-valued data. FUZZ-IEEE 2010: 1-8

Mika Sato-Ilic, Shota Ito, Shota Takahashi: Generalized kernel fuzzy clustering model. FUZZ-IEEE 2009: 421-426

Lakhmi C. Jain, Mika Sato-Ilic, Maria Virvou, George A. Tsihrintzis, Valentina Emilia Balas, Canicious Abeynayake (Eds.): Computational Intelligence Paradigms, Innovative Applications. Studies in Computational Intelligence 137, Springer 2008, isbn 978-3-540-79473-8

Mika Sato-Ilic: Clustering objects with degree of classification. FUZZ-IEEE 2008: 1737-1744

Mika Sato-Ilic: Fuzzy Blocking Regression Models. Computational Intelligence Paradigms 2008: 195-217

Javaan S. Chahl, Lakhmi C. Jain, Akiko Mizutani, Mika Sato-Ilic (Eds.): Innovations in Intelligent Machines - 1. Studies in Computational Intelligence 70, Springer 2007, isbn 978-3-540-72695-1

Mika Sato-Ilic, Lakhmi C. Jain: Asymmetric Clustering Based on Self-Similarity. IIH-MSP 2007: 361-364

Mika Sato-Ilic: Weighted fuzzy clustering on subsets of variables. ISSPA 2007: 1-4

Mika Sato-Ilic: Variable Based Fuzzy Blocking Regression Model. KES (2) 2007: 525-532

Mika Sato-Ilic, Lakhmi C. Jain: Innovations in Fuzzy Clustering - Theory and Applications. Studies in Fuzziness and Soft Computing 205, Springer 2006, isbn 978-3-540-34356-1, pp. 1-145

Mika Sato-Ilic, Tomoyuki Kuwata: Fuzzy Structural Classification Methods. KES (2) 2006: 102-109

Mika Sato-Ilic, Lakhmi C. Jain: Innovations in Soft Data Analysis. KES (2) 2006: 110-114

Mika Sato-Ilic: Fuzzy clustering based weighted principal component analysis for interval-valued data considering uniqueness of clusters. SMC (3) 2004: 2297-2302

Mika Sato-Ilic: Weighted principal component analysis for interval-valued data based on fuzzy clustering. SMC 2003: 4476-4482

Mika Sato-Ilic: Classification Based on Relational Fuzzy C-Means for 3-Way Data. ICEIS 2000: 217-221

Mika Sato-Ilic: On Dynamic Clustering Models for 3-way Data. JACIII 3(1): 28-35 (1999)

Mika Sato, Yoshiharu Sato, Lakhmi C. Jain: Fuzzy clustering models and applications. Studies in Fuzziness and Soft Computing 9, Springer 1997, isbn 978-3-7908-1026-4, pp. I-IX, 1-122

Mika Sato, Y. Sato, Lakhmi C. Jain: General fuzzy clustering model and neural networks. Electronic Technology Directions 1995: 104-112