An Agent Control Method Based on Variable Neighborhoods. - Seiki Ubukata; Yasuo Kudo; Tetsuya Murai (KES (2), 2009)

A Multi-agent System Based on Variable Neighborhood Model. - Seiki Ubukata; Yasuo Kudo; Tetsuya Murai (GrC, 2010)

Granularity and Approximation in Sequences, Multisets, and Sets in the Framework of Kripke Semantics. - Tetsuya Murai; Seiki Ubukata; Yasuo Kudo; Seiki Akama; Sadaaki Miyamoto (IUM, 2010)

Agents' KANSEI expression based on variable neighborhood models. - Seiki Ubukata; Tetsuya Murai; Yasuo Kudo (GrC, 2011)

Autonomous agent control based on variable neighbourhoods. - Seiki Ubukata; Yasuo Kudo; Tetsuya Murai (Int. J. Reason. based Intell. Syst., 2011)

Variable Neighborhood Model for Agent Control Introducing Accessibility Relations Between Agents with Linear Temporal Logic. - Seiki Ubukata; Tetsuya Murai; Yasuo Kudo; Seiki Akama (J. Adv. Comput. Intell. Intell. Informatics, 2014)

Performance Investigation of UCB Policy in Q-learning. - Koki Saito; Akira Notsu; Seiki Ubukata; Katsuhiro Honda (ICMLA, 2015)

MMMs-Induced Fuzzy Co-clustering with Exclusive Partition Penalty on Selected Items. - Takaya Nakano; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (IUKM, 2015)

Proposal of Grid Area Search with UCB for Discrete Optimization Problem. - Akira Notsu; Koki Saito; Yuhumi Nohara; Seiki Ubukata; Katsuhiro Honda (IUKM, 2015)

FCM-Type Co-clustering Transfer Reinforcement Learning for Non-Markov Processes. - Akira Notsu; Takanori Ueno; Yuichi Hattori; Seiki Ubukata; Katsuhiro Honda (IUKM, 2015)

A Deterministic Clustering Framework in MMMs-Induced Fuzzy Co-clustering. - Shunnya Oshio; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (IUKM, 2015)

An Ensemble Learning Approach Based on Rough Set Preserving the Qualities of Approximations. - Seiki Ubukata; Taro Miyazaki; Akira Notsu; Katsuhiro Honda; Masahiro Inuiguchi (IUKM, 2015)

An Ensemble Learning Approach Based on Missing-Valued Tables. - Seiki Ubukata; Taro Miyazaki; Akira Notsu; Katsuhiro Honda; Masahiro Inuiguchi (RSFDGrC, 2015)

Imprecise Rules for Data Privacy. - Masahiro Inuiguchi; Takuya Hamakawa; Seiki Ubukata (RSKT, 2015)

A study on fuzzy clustering-based k-anonymization for privacy preserving crowd movement analysis with face recognition. - Katsuhiro Honda; Masahiro Omori; Seiki Ubukata; Akira Notsu (SoCPaR, 2015)

Fuzzy co-clustering considering site-wise confidence of vertically partitioned cooccurrence data. - Toshiya Oda; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (TAAI, 2015)

A study on fuzzy co-clustering with partial supervision and virtual samples. - Daiji Tanaka; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (TAAI, 2015)

Partially Exclusive Item Partition in MMMs-Induced Fuzzy Co-Clustering and its Effects in Collaborative Filtering. - Katsuhiro Honda; Takaya Nakano; Chi-Hyon Oh; Seiki Ubukata; Akira Notsu (J. Adv. Comput. Intell. Intell. Informatics, 2015)

A fuzzy co-clustering interpretation of probabilistic latent semantic analysis. - Katsuhiro Honda; Takafumi Goshima; Seiki Ubukata; Akira Notsu (FUZZ-IEEE, 2016)

MMMs-induced k-member co-clustering for k-anonymization of cooccurrence information. - Katsuhiro Honda; Hikaru Sakamoto; Seiki Ubukata; Akira Notsu (IJCNN, 2016)

Fuzzy DA Clustering-Based Improvement of Probabilistic Latent Semantic Analysis. - Takafumi Goshima; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (IUKM, 2016)

Exclusive Item Partition with Fuzziness Tuning in MMMs-Induced Fuzzy Co-clustering. - Takaya Nakano; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (IUKM, 2016)

The Rough Membership k-Means Clustering. - Seiki Ubukata; Akira Notsu; Katsuhiro Honda (IUKM, 2016)

Cluster Validation in Multinomial Mixtures-Induced Fuzzy Co-Clustering. - Katsuhiro Honda; Yurina Suzuki; Seiki Ubukata; Akira Notsu (SCIS&ISIS, 2016)

A Noise Fuzzy Co-Clustering Scheme in MMMs-Induced Clustering. - Katsuhiro Honda; Nami Yamamoto; Seiki Ubukata; Akira Notsu (SCIS&ISIS, 2016)

A Study on Recommendation Ability in Collaborative Filtering by Fuzzy Co-Clustering with Exclusive Item Partition. - Takaya Nakano; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (SCIS&ISIS, 2016)

Application of the UCT Algorithm for Noisy Optimization Problems. - Akira Notsu; Satoshi Kane; Seiki Ubukata; Katsuhiro Honda (SCIS&ISIS, 2016)

The Rough Set k-Means Clustering. - Seiki Ubukata; Akira Notsu; Katsuhiro Honda (SCIS&ISIS, 2016)

A Semi-Supervised Framework for MMMs-Induced Fuzzy Co-Clustering with Virtual Samples. - Daiji Tanaka; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (Adv. Fuzzy Syst., 2016)

Visualization of Learning Process in "State and Action" Space Using Self-Organizing Maps. - Akira Notsu; Yuichi Hattori; Seiki Ubukata; Katsuhiro Honda (J. Adv. Comput. Intell. Intell. Informatics, 2016)

A fuzzy co-clustering model for three-modes relational cooccurrence data. - Katsuhiro Honda; Yurina Suzuki; Mio Nishioka; Seiki Ubukata; Akira Notsu (FUZZ-IEEE, 2017)

Noise rejection schemes for FCM-type co-clustering based on uniform noise distribution. - Nami Yamamoto; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (FUZZ-IEEE, 2017)

Visual assessment of co-cluster structure through cooccurrence-sensitive ordering. - Katsuhiro Honda; Takuya Sako; Seiki Ubukata; Akira Notsu (IFSA-SCIS, 2017)

A novel approach to noise clustering in multivariate fuzzy c-Means. - Katsuhiro Honda; Seiki Ubukata; Akira Notsu (IFSA-SCIS, 2017)

Possibilistic co-clustering based on extension of noise rejection scheme in FCCMM. - Seiki Ubukata; Katsuya Koike; Akira Notsu; Katsuhiro Honda (IFSA-SCIS, 2017)

FCM-Type Fuzzy Coclustering for Three-Mode Cooccurrence Data: 3FCCM and 3Fuzzy CoDoK. - Katsuhiro Honda; Yurina Suzuki; Seiki Ubukata; Akira Notsu (Adv. Fuzzy Syst., 2017)

Noise Rejection in MMMs-Induced Fuzzy Co-Clustering. - Katsuhiro Honda; Nami Yamamoto; Seiki Ubukata; Akira Notsu (J. Adv. Comput. Intell. Intell. Informatics, 2017)

Setting of Candidate Solutions Considering Confidence Intervals in Differential Evolution. - Akira Notsu; Masaya Sakakibara; Seiki Ubukata; Katsuhiro Honda (iFUZZY, 2018)

Basic Consideration of Online and Mini-Batch Algorithms for MMMs-induced Fuzzy Co-clustering. - Seiki Ubukata; Akira Notsu; Keiko Kida; Katsuhiro Honda (iFUZZY, 2018)

Effects of Semi-supervised Learning on Rough Set-Based C-Means Clustering. - Seiki Ubukata; Takeaki Shimizu; Akira Notsu; Katsuhiro Honda (iFUZZY, 2018)

Privacy Preserving Collaborative Fuzzy Co-clustering of Three-Mode Cooccurrence Data. - Katsuhiro Honda; Shotaro Matsuzaki; Seiki Ubukata; Akira Notsu (MDAI, 2018)

Automatic Estimation of Cluster Number in Fuzzy Co-Clustering Based on Competition and Elimination of Clusters. - Seiki Ubukata; Kazuki Yanagisawa; Akira Notsu; Katsuhiro Honda (SCIS&ISIS, 2018)

Optimization of Learning Cycles in Online Reinforcement Learning Systems. - Akira Notsu; Koji Yasuda; Seiki Ubukata; Katsuhiro Honda (SMC, 2018)

Deterministic annealing process for pLSA-induced fuzzy co-clustering and cluster splitting characteristics. - Takafumi Goshima; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (Int. J. Approx. Reason., 2018)

Visual Co-Cluster Assessment with Intuitive Cluster Validation Through Cooccurrence-Sensitive Ordering. - Katsuhiro Honda; Takuya Sako; Seiki Ubukata; Akira Notsu (J. Adv. Comput. Intell. Intell. Informatics, 2018)

MMMs-Induced Possibilistic Fuzzy Co-Clustering and its Characteristics. - Seiki Ubukata; Katsuya Koike; Akira Notsu; Katsuhiro Honda (J. Adv. Comput. Intell. Intell. Informatics, 2018)

Rough Set-Based Clustering Utilizing Probabilistic Memberships. - Seiki Ubukata; Hiroki Kato; Akira Notsu; Katsuhiro Honda (J. Adv. Comput. Intell. Intell. Informatics, 2018)

Characteristics of Rough Set C-Means Clustering. - Seiki Ubukata; Keisuke Umado; Akira Notsu; Katsuhiro Honda (J. Adv. Comput. Intell. Intell. Informatics, 2018)

Robust Non-negative Matrix Factorization Based on Noise Fuzzy Clustering Mechanism. - Masaaki Ueno; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (AICCC, 2019)

Comparison of Gradient Descent Methods in Online Fuzzy Co-clustering. - Keiko Kida; Seiki Ubukata; Akira Notsu; Katsuhiro Honda (iFUZZY, 2019)

Fuzzy c-Regression Models with Cluster Characteristics Clarification. - Shinpei Nasada; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (iFUZZY, 2019)

Effects of Semi-supervised Learning on Rough Membership C- Means Clustering. - Takeaki Shimizu; Seiki Ubukata; Akira Notsu; Katsuhiro Honda (iFUZZY, 2019)

A Comparative Study on Questionnaire Design for Categorization Based on Fuzzy Co-clustering Concept and Multi-view Possibilistic Partition. - Ruixin Yang; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (iFUZZY, 2019)

Fuzzy Co-clustering for Categorization of Subjects in Questionnaire Considering Responsibility of Each Question. - Katsuhiro Honda; Ruixin Yang; Seiki Ubukata; Akira Notsu (IUKM, 2019)

Designation of Candidate Solutions in Differential Evolution Based on Bandit Algorithm and its Evaluation. - Masaya Sakakibara; Akira Notsu; Seiki Ubukata; Katsuhiro Honda (J. Adv. Comput. Intell. Intell. Informatics, 2019)

A unified approach for cluster-wise and general noise rejection approaches for k-means clustering. - Seiki Ubukata (PeerJ Comput. Sci., 2019)

A Comparative Study on Three-mode Fuzzy Co-clustering Based on Co-occurrence Aggregation Criteria. - Katsuhiro Honda; Issei Hayashi; Seiki Ubukata; Akira Notsu (CcS, 2020)

Proposal of Adaptive Randomness in Differential Evolution. - Junya Tsubamoto; Akira Notsu; Seiki Ubukata; Katsuhiro Honda (CEC, 2020)

A Noise Rejection Mechanism for pLSA-induced Fuzzy Co-clustering. - Katsuhiro Honda; Keita Hoshii; Seiki Ubukata; Akira Notsu (FUZZ-IEEE, 2020)

Basic Consideration of Co-Clustering Based on Rough Set Theory. - Seiki Ubukata; Narihira Nodake; Akira Notsu; Katsuhiro Honda (IUKM, 2020)

Randomness Selection in Differential Evolution Using Thompson Sampling. - Akira Notsu; Junya Tsubamoto; Yuichi Miyahira; Seiki Ubukata; Katsuhiro Honda (SCIS/ISIS, 2020)

Basic Consideration of Rough C-Medoids Clustering with Minkowski Distance. - Seiki Ubukata; Atsushi Sugimoto; Akira Notsu; Katsuhiro Honda (SCIS/ISIS, 2020)

Basic Consideration of Collaborative Filtering Based on Rough C-Means Clustering. - Seiki Ubukata; Shu Takahashi; Akira Notsu; Katsuhiro Honda (SCIS/ISIS, 2020)

Online state space generation by a growing self-organizing map and differential learning for reinforcement learning. - Akira Notsu; Koji Yasuda; Seiki Ubukata; Katsuhiro Honda (Appl. Soft Comput., 2020)

A Heuristic-Based Model for MMMs-Induced Fuzzy Co-Clustering with Dual Exclusive Partition. - Katsuhiro Honda; Yoshiki Hakui; Seiki Ubukata; Akira Notsu (J. Adv. Comput. Intell. Intell. Informatics, 2020)

Noise Rejection Approaches for Various Rough Set-Based C-Means Clustering. - Seiki Ubukata; Sho Sekiya; Akira Notsu; Katsuhiro Honda (J. Adv. Comput. Intell. Intell. Informatics, 2020)

Fuzzy Clustering-based Switching Non-negative Matrix Factorization and Its Application to Environmental Data Analysis. - Katsuhiro Honda; T. Furukawa; Seiki Ubukata; Akira Notsu (IFSA/EUSFLAT/AGOP, 2021)

Fuzzy c-Varieties Clustering for Vertically Distributed Datasets. - Katsuhiro Honda; Kohei Kunisawa; Seiki Ubukata; Akira Notsu (KES, 2021)

Fuzzy-Possibilistic Clustering for Categorical Multivariate Data. - Katsuhiro Honda; Kosuke Hayashi; Seiki Ubukata; Akira Notsu (SICE, 2021)

Objective function-based rough membership C-means clustering. - Seiki Ubukata; Akira Notsu; Katsuhiro Honda (Inf. Sci., 2021)

Three-Mode Fuzzy Co-Clustering Based on Probabilistic Concept and Comparison with FCM-Type Algorithms. - Katsuhiro Honda; Issei Hayashi; Seiki Ubukata; Akira Notsu (J. Adv. Comput. Intell. Intell. Informatics, 2021)

A Noise Clustering-induced Robust Adaptive Network-based Fuzzy Inference System for Classification. - Katsuhiro Honda; Koki Kitamori; Seiki Ubukata; Akira Notsu (IJCNN, 2022)

Noise Fuzzy Clustering-Based Robust Non-negative Matrix Factorization with I-divergence Criterion. - Akira Okabe; Katsuhiro Honda; Seiki Ubukata (IUKM, 2022)

A Comparative Study on Utilization of Semantic Information in Fuzzy Co-clustering. - Yusuke Takahata; Katsuhiro Honda; Seiki Ubukata (IUKM, 2022)

Handling of Missing Values in FCM Clustering-based ANFIS with Partial Distance Strategy. - Katsuhiro Honda; Satoshi Hyakutake; Seiki Ubukata; Akira Notsu (SCIS/ISIS, 2022)

Fuzzy c-Lines for Vertically Distributed Database with Missing Values. - Kohei Kunisawa; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (SCIS/ISIS, 2022)

Basic Consideration of Collaborative Filtering Based on Rough Co-clustering Induced by Multinomial Mixture Models. - Seiki Ubukata; Kenryu Mouri; Katsuhiro Honda (SCIS/ISIS, 2022)

Adaptive Online Rough C-Means Clustering and Its Application to Collaborative Filtering. - Seiki Ubukata; Tomohiro Kawakami; Katsuhiro Honda (SSCI, 2022)

FCM-Induced Switching Fuzzy Factorization Machine for Collaborative Filtering. - Rikuto Daido; Katsuhiro Honda; Seiki Ubukata; Akira Notsu (FUZZ, 2023)

Handling of Component-Wise Noise in ANFIS Induced by Ellipsoidal Fuzzy Clustering. - Katsuhiro Honda; Ryuta Kurahashi; Seiki Ubukata; Akira Notsu (FUZZ, 2023)

Collaborative Filtering Based on Probabilistic Rough Set C-Means Clustering. - Seiki Ubukata; Kazuma Ehara (IUKM (2), 2023)

Collaborative Filtering Based on Rough C-Means Clustering with Missing Value Processing. - Seiki Ubukata; Kazushi Futakuchi (IUKM (2), 2023)

A comparative study on effects of some exclusive conditions in fuzzy co-clustering for collaborative filtering. - Katsuhiro Honda; Seiki Ubukata; Akira Notsu (J. Ambient Intell. Humaniz. Comput., 2023)

Spherical Rough C-Means Clustering and Its Application to Collaborative Filtering. - Seiki Ubukata; Daiki Io; Katsuhiro Honda (ICMLC, 2024)

Federated Rough C-Means Clustering and Its Application to Collaborative Filtering. - Seiki Ubukata; Shuichiro Yamashita; Katsuhiro Honda (ICMLC, 2024)

FCM-Induced Switching Reinforcement Learning for Collaborative Learning. - Katsuhiro Honda; Taimu Yaotome; Seiki Ubukata; Akira Notsu (IJCNN, 2024)

Utilization of Genre Information in Collaborative Filtering by Three-mode Fuzzy Co-Clustering. - Katsuhiro Honda; Haruto Miwa; Seiki Ubukata; Akira Notsu (SCIS/ISIS, 2024)

Extension of Fuzzy c-Lines Considering Compactness and Separation of Clusters. - Katsuhiro Honda; Daichi Machida; Seiki Ubukata; Akira Notsu (SCIS/ISIS, 2024)

A Local PCA Model Induced from Linear Fuzzy Clustering with Cluster Separation. - Katsuhiro Honda; Daichi Machida; Seiki Ubukata; Akira Notsu (IUKM (1), 2025)