Остановите войну!
for scientists:
default search action
Seiki Ubukata
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2023
- [j20]Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
A comparative study on effects of some exclusive conditions in fuzzy co-clustering for collaborative filtering. J. Ambient Intell. Humaniz. Comput. 14(11): 14589-14594 (2023) - [c62]Rikuto Daido, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
FCM-Induced Switching Fuzzy Factorization Machine for Collaborative Filtering. FUZZ 2023: 1-6 - [c61]Katsuhiro Honda, Ryuta Kurahashi, Seiki Ubukata, Akira Notsu:
Handling of Component-Wise Noise in ANFIS Induced by Ellipsoidal Fuzzy Clustering. FUZZ 2023: 1-6 - [c60]Seiki Ubukata, Kazushi Futakuchi:
Collaborative Filtering Based on Rough C-Means Clustering with Missing Value Processing. IUKM (2) 2023: 218-229 - [c59]Seiki Ubukata, Kazuma Ehara:
Collaborative Filtering Based on Probabilistic Rough Set C-Means Clustering. IUKM (2) 2023: 230-242 - 2022
- [c58]Katsuhiro Honda, Koki Kitamori, Seiki Ubukata, Akira Notsu:
A Noise Clustering-induced Robust Adaptive Network-based Fuzzy Inference System for Classification. IJCNN 2022: 1-7 - [c57]Yusuke Takahata, Katsuhiro Honda, Seiki Ubukata:
A Comparative Study on Utilization of Semantic Information in Fuzzy Co-clustering. IUKM 2022: 216-225 - [c56]Akira Okabe, Katsuhiro Honda, Seiki Ubukata:
Noise Fuzzy Clustering-Based Robust Non-negative Matrix Factorization with I-divergence Criterion. IUKM 2022: 256-266 - [c55]Katsuhiro Honda, Satoshi Hyakutake, Seiki Ubukata, Akira Notsu:
Handling of Missing Values in FCM Clustering-based ANFIS with Partial Distance Strategy. SCIS/ISIS 2022: 1-6 - [c54]Kohei Kunisawa, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
Fuzzy c-Lines for Vertically Distributed Database with Missing Values. SCIS/ISIS 2022: 1-6 - [c53]Seiki Ubukata, Kenryu Mouri, Katsuhiro Honda:
Basic Consideration of Collaborative Filtering Based on Rough Co-clustering Induced by Multinomial Mixture Models. SCIS/ISIS 2022: 1-6 - [c52]Seiki Ubukata, Tomohiro Kawakami, Katsuhiro Honda:
Adaptive Online Rough C-Means Clustering and Its Application to Collaborative Filtering. SSCI 2022: 368-373 - [e1]Katsuhiro Honda, Tomoe Entani, Seiki Ubukata, Van-Nam Huynh, Masahiro Inuiguchi:
Integrated Uncertainty in Knowledge Modelling and Decision Making - 9th International Symposium, IUKM 2022, Ishikawa, Japan, March 18-19, 2022, Proceedings. Lecture Notes in Computer Science 13199, Springer 2022, ISBN 978-3-030-98017-7 [contents] - 2021
- [j19]Seiki Ubukata, Akira Notsu, Katsuhiro Honda:
Objective function-based rough membership C-means clustering. Inf. Sci. 548: 479-496 (2021) - [j18]Katsuhiro Honda, Issei Hayashi, Seiki Ubukata, Akira Notsu:
Three-Mode Fuzzy Co-Clustering Based on Probabilistic Concept and Comparison with FCM-Type Algorithms. J. Adv. Comput. Intell. Intell. Informatics 25(4): 478-488 (2021) - [c51]Katsuhiro Honda, Kohei Kunisawa, Seiki Ubukata, Akira Notsu:
Fuzzy c-Varieties Clustering for Vertically Distributed Datasets. KES 2021: 457-466 - [c50]Katsuhiro Honda, Kosuke Hayashi, Seiki Ubukata, Akira Notsu:
Fuzzy-Possibilistic Clustering for Categorical Multivariate Data. SICE 2021: 9-14 - 2020
- [j17]Akira Notsu, Koji Yasuda, Seiki Ubukata, Katsuhiro Honda:
Online state space generation by a growing self-organizing map and differential learning for reinforcement learning. Appl. Soft Comput. 97(Part B): 106723 (2020) - [j16]Katsuhiro Honda, Yoshiki Hakui, Seiki Ubukata, Akira Notsu:
A Heuristic-Based Model for MMMs-Induced Fuzzy Co-Clustering with Dual Exclusive Partition. J. Adv. Comput. Intell. Intell. Informatics 24(1): 40-47 (2020) - [j15]Seiki Ubukata, Sho Sekiya, Akira Notsu, Katsuhiro Honda:
Noise Rejection Approaches for Various Rough Set-Based C-Means Clustering. J. Adv. Comput. Intell. Intell. Informatics 24(6): 738-749 (2020) - [c49]Katsuhiro Honda, Issei Hayashi, Seiki Ubukata, Akira Notsu:
A Comparative Study on Three-mode Fuzzy Co-clustering Based on Co-occurrence Aggregation Criteria. CcS 2020: 1-6 - [c48]Junya Tsubamoto, Akira Notsu, Seiki Ubukata, Katsuhiro Honda:
Proposal of Adaptive Randomness in Differential Evolution. CEC 2020: 1-8 - [c47]Katsuhiro Honda, Keita Hoshii, Seiki Ubukata, Akira Notsu:
A Noise Rejection Mechanism for pLSA-induced Fuzzy Co-clustering. FUZZ-IEEE 2020: 1-8 - [c46]Seiki Ubukata, Narihira Nodake, Akira Notsu, Katsuhiro Honda:
Basic Consideration of Co-Clustering Based on Rough Set Theory. IUKM 2020: 151-161 - [c45]Akira Notsu, Junya Tsubamoto, Yuichi Miyahira, Seiki Ubukata, Katsuhiro Honda:
Randomness Selection in Differential Evolution Using Thompson Sampling. SCIS/ISIS 2020: 1-5 - [c44]Seiki Ubukata, Atsushi Sugimoto, Akira Notsu, Katsuhiro Honda:
Basic Consideration of Rough C-Medoids Clustering with Minkowski Distance. SCIS/ISIS 2020: 1-6 - [c43]Seiki Ubukata, Shu Takahashi, Akira Notsu, Katsuhiro Honda:
Basic Consideration of Collaborative Filtering Based on Rough C-Means Clustering. SCIS/ISIS 2020: 1-6
2010 – 2019
- 2019
- [j14]Masaya Sakakibara, Akira Notsu, Seiki Ubukata, Katsuhiro Honda:
Designation of Candidate Solutions in Differential Evolution Based on Bandit Algorithm and its Evaluation. J. Adv. Comput. Intell. Intell. Informatics 23(4): 758-766 (2019) - [j13]Seiki Ubukata:
A unified approach for cluster-wise and general noise rejection approaches for k-means clustering. PeerJ Comput. Sci. 5: e238 (2019) - [c42]Masaaki Ueno, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
Robust Non-negative Matrix Factorization Based on Noise Fuzzy Clustering Mechanism. AICCC 2019: 1-5 - [c41]Ruixin Yang, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
A Comparative Study on Questionnaire Design for Categorization Based on Fuzzy Co-clustering Concept and Multi-view Possibilistic Partition. iFUZZY 2019: 1-4 - [c40]Shinpei Nasada, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
Fuzzy c-Regression Models with Cluster Characteristics Clarification. iFUZZY 2019: 5-8 - [c39]Keiko Kida, Seiki Ubukata, Akira Notsu, Katsuhiro Honda:
Comparison of Gradient Descent Methods in Online Fuzzy Co-clustering. iFUZZY 2019: 9-14 - [c38]Takeaki Shimizu, Seiki Ubukata, Akira Notsu, Katsuhiro Honda:
Effects of Semi-supervised Learning on Rough Membership C- Means Clustering. iFUZZY 2019: 15-20 - [c37]Katsuhiro Honda, Ruixin Yang, Seiki Ubukata, Akira Notsu:
Fuzzy Co-clustering for Categorization of Subjects in Questionnaire Considering Responsibility of Each Question. IUKM 2019: 370-379 - 2018
- [j12]Takafumi Goshima, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
Deterministic annealing process for pLSA-induced fuzzy co-clustering and cluster splitting characteristics. Int. J. Approx. Reason. 95: 185-193 (2018) - [j11]Seiki Ubukata, Keisuke Umado, Akira Notsu, Katsuhiro Honda:
Characteristics of Rough Set C-Means Clustering. J. Adv. Comput. Intell. Intell. Informatics 22(4): 551-564 (2018) - [j10]Katsuhiro Honda, Takuya Sako, Seiki Ubukata, Akira Notsu:
Visual Co-Cluster Assessment with Intuitive Cluster Validation Through Cooccurrence-Sensitive Ordering. J. Adv. Comput. Intell. Intell. Informatics 22(5): 585-592 (2018) - [j9]Seiki Ubukata, Katsuya Koike, Akira Notsu, Katsuhiro Honda:
MMMs-Induced Possibilistic Fuzzy Co-Clustering and its Characteristics. J. Adv. Comput. Intell. Intell. Informatics 22(5): 747-758 (2018) - [j8]Seiki Ubukata, Hiroki Kato, Akira Notsu, Katsuhiro Honda:
Rough Set-Based Clustering Utilizing Probabilistic Memberships. J. Adv. Comput. Intell. Intell. Informatics 22(6): 956-964 (2018) - [c36]Akira Notsu, Masaya Sakakibara, Seiki Ubukata, Katsuhiro Honda:
Setting of Candidate Solutions Considering Confidence Intervals in Differential Evolution. iFUZZY 2018: 7-11 - [c35]Seiki Ubukata, Takeaki Shimizu, Akira Notsu, Katsuhiro Honda:
Effects of Semi-supervised Learning on Rough Set-Based C-Means Clustering. iFUZZY 2018: 12-17 - [c34]Seiki Ubukata, Akira Notsu, Keiko Kida, Katsuhiro Honda:
Basic Consideration of Online and Mini-Batch Algorithms for MMMs-induced Fuzzy Co-clustering. iFUZZY 2018: 85-90 - [c33]Katsuhiro Honda, Shotaro Matsuzaki, Seiki Ubukata, Akira Notsu:
Privacy Preserving Collaborative Fuzzy Co-clustering of Three-Mode Cooccurrence Data. MDAI 2018: 232-242 - [c32]Seiki Ubukata, Kazuki Yanagisawa, Akira Notsu, Katsuhiro Honda:
Automatic Estimation of Cluster Number in Fuzzy Co-Clustering Based on Competition and Elimination of Clusters. SCIS&ISIS 2018: 660-665 - [c31]Akira Notsu, Koji Yasuda, Seiki Ubukata, Katsuhiro Honda:
Optimization of Learning Cycles in Online Reinforcement Learning Systems. SMC 2018: 3530-3534 - 2017
- [j7]Katsuhiro Honda, Yurina Suzuki, Seiki Ubukata, Akira Notsu:
FCM-Type Fuzzy Coclustering for Three-Mode Cooccurrence Data: 3FCCM and 3Fuzzy CoDoK. Adv. Fuzzy Syst. 2017: 9842127:1-9842127:8 (2017) - [j6]Katsuhiro Honda, Nami Yamamoto, Seiki Ubukata, Akira Notsu:
Noise Rejection in MMMs-Induced Fuzzy Co-Clustering. J. Adv. Comput. Intell. Intell. Informatics 21(7): 1144-1151 (2017) - [c30]Katsuhiro Honda, Yurina Suzuki, Mio Nishioka, Seiki Ubukata, Akira Notsu:
A fuzzy co-clustering model for three-modes relational cooccurrence data. FUZZ-IEEE 2017: 1-6 - [c29]Nami Yamamoto, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
Noise rejection schemes for FCM-type co-clustering based on uniform noise distribution. FUZZ-IEEE 2017: 1-6 - [c28]Katsuhiro Honda, Takuya Sako, Seiki Ubukata, Akira Notsu:
Visual assessment of co-cluster structure through cooccurrence-sensitive ordering. IFSA-SCIS 2017: 1-6 - [c27]Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
A novel approach to noise clustering in multivariate fuzzy c-Means. IFSA-SCIS 2017: 1-4 - [c26]Seiki Ubukata, Katsuya Koike, Akira Notsu, Katsuhiro Honda:
Possibilistic co-clustering based on extension of noise rejection scheme in FCCMM. IFSA-SCIS 2017: 1-6 - 2016
- [j5]Daiji Tanaka, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
A Semi-Supervised Framework for MMMs-Induced Fuzzy Co-Clustering with Virtual Samples. Adv. Fuzzy Syst. 2016: 5206048:1-5206048:8 (2016) - [j4]Akira Notsu, Yuichi Hattori, Seiki Ubukata, Katsuhiro Honda:
Visualization of Learning Process in "State and Action" Space Using Self-Organizing Maps. J. Adv. Comput. Intell. Intell. Informatics 20(6): 983-991 (2016) - [c25]Katsuhiro Honda, Takafumi Goshima, Seiki Ubukata, Akira Notsu:
A fuzzy co-clustering interpretation of probabilistic latent semantic analysis. FUZZ-IEEE 2016: 718-723 - [c24]Katsuhiro Honda, Hikaru Sakamoto, Seiki Ubukata, Akira Notsu:
MMMs-induced k-member co-clustering for k-anonymization of cooccurrence information. IJCNN 2016: 2961-2966 - [c23]Takafumi Goshima, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
Fuzzy DA Clustering-Based Improvement of Probabilistic Latent Semantic Analysis. IUKM 2016: 175-184 - [c22]Takaya Nakano, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
Exclusive Item Partition with Fuzziness Tuning in MMMs-Induced Fuzzy Co-clustering. IUKM 2016: 185-194 - [c21]Seiki Ubukata, Akira Notsu, Katsuhiro Honda:
The Rough Membership k-Means Clustering. IUKM 2016: 207-216 - [c20]Akira Notsu, Satoshi Kane, Seiki Ubukata, Katsuhiro Honda:
Application of the UCT Algorithm for Noisy Optimization Problems. SCIS&ISIS 2016: 48-52 - [c19]Seiki Ubukata, Akira Notsu, Katsuhiro Honda:
The Rough Set k-Means Clustering. SCIS&ISIS 2016: 189-193 - [c18]Takaya Nakano, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
A Study on Recommendation Ability in Collaborative Filtering by Fuzzy Co-Clustering with Exclusive Item Partition. SCIS&ISIS 2016: 686-689 - [c17]Katsuhiro Honda, Yurina Suzuki, Seiki Ubukata, Akira Notsu:
Cluster Validation in Multinomial Mixtures-Induced Fuzzy Co-Clustering. SCIS&ISIS 2016: 690-694 - [c16]Katsuhiro Honda, Nami Yamamoto, Seiki Ubukata, Akira Notsu:
A Noise Fuzzy Co-Clustering Scheme in MMMs-Induced Clustering. SCIS&ISIS 2016: 695-699 - 2015
- [j3]Katsuhiro Honda, Takaya Nakano, Chi-Hyon Oh, Seiki Ubukata, Akira Notsu:
Partially Exclusive Item Partition in MMMs-Induced Fuzzy Co-Clustering and its Effects in Collaborative Filtering. J. Adv. Comput. Intell. Intell. Informatics 19(6): 810-817 (2015) - [c15]Koki Saito, Akira Notsu, Seiki Ubukata, Katsuhiro Honda:
Performance Investigation of UCB Policy in Q-learning. ICMLA 2015: 777-780 - [c14]Akira Notsu, Koki Saito, Yuhumi Nohara, Seiki Ubukata, Katsuhiro Honda:
Proposal of Grid Area Search with UCB for Discrete Optimization Problem. IUKM 2015: 102-111 - [c13]Shunnya Oshio, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
A Deterministic Clustering Framework in MMMs-Induced Fuzzy Co-clustering. IUKM 2015: 204-213 - [c12]Akira Notsu, Takanori Ueno, Yuichi Hattori, Seiki Ubukata, Katsuhiro Honda:
FCM-Type Co-clustering Transfer Reinforcement Learning for Non-Markov Processes. IUKM 2015: 214-225 - [c11]Takaya Nakano, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
MMMs-Induced Fuzzy Co-clustering with Exclusive Partition Penalty on Selected Items. IUKM 2015: 226-235 - [c10]Seiki Ubukata, Taro Miyazaki, Akira Notsu, Katsuhiro Honda, Masahiro Inuiguchi:
An Ensemble Learning Approach Based on Rough Set Preserving the Qualities of Approximations. IUKM 2015: 247-253 - [c9]Seiki Ubukata, Taro Miyazaki, Akira Notsu, Katsuhiro Honda, Masahiro Inuiguchi:
An Ensemble Learning Approach Based on Missing-Valued Tables. RSFDGrC 2015: 310-321 - [c8]Masahiro Inuiguchi, Takuya Hamakawa, Seiki Ubukata:
Imprecise Rules for Data Privacy. RSKT 2015: 129-139 - [c7]Katsuhiro Honda, Masahiro Omori, Seiki Ubukata, Akira Notsu:
A study on fuzzy clustering-based k-anonymization for privacy preserving crowd movement analysis with face recognition. SoCPaR 2015: 37-41 - [c6]Toshiya Oda, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
Fuzzy co-clustering considering site-wise confidence of vertically partitioned cooccurrence data. TAAI 2015: 404-407 - [c5]Daiji Tanaka, Katsuhiro Honda, Seiki Ubukata, Akira Notsu:
A study on fuzzy co-clustering with partial supervision and virtual samples. TAAI 2015: 408-411 - 2014
- [j2]Seiki Ubukata, Tetsuya Murai, Yasuo Kudo, Seiki Akama:
Variable Neighborhood Model for Agent Control Introducing Accessibility Relations Between Agents with Linear Temporal Logic. J. Adv. Comput. Intell. Intell. Informatics 18(6): 937-945 (2014) - 2011
- [j1]Seiki Ubukata, Yasuo Kudo, Tetsuya Murai:
Autonomous agent control based on variable neighbourhoods. Int. J. Reason. based Intell. Syst. 3(1): 8-13 (2011) - [c4]Seiki Ubukata, Tetsuya Murai, Yasuo Kudo:
Agents' KANSEI expression based on variable neighborhood models. GrC 2011: 687-690 - 2010
- [c3]Seiki Ubukata, Yasuo Kudo, Tetsuya Murai:
A Multi-agent System Based on Variable Neighborhood Model. GrC 2010: 495-498 - [c2]Tetsuya Murai, Seiki Ubukata, Yasuo Kudo, Seiki Akama, Sadaaki Miyamoto:
Granularity and Approximation in Sequences, Multisets, and Sets in the Framework of Kripke Semantics. IUM 2010: 329-334
2000 – 2009
- 2009
- [c1]Seiki Ubukata, Yasuo Kudo, Tetsuya Murai:
An Agent Control Method Based on Variable Neighborhoods. KES (2) 2009: 356-363
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from , , and to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-04-25 05:43 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint