Yuchi Kanzawa

Research output

Research output per year 1994 1999 2008 2009 2012 2014 2015 2019 2022 2022

• 63 Article
• 40 Conference contribution
• 2 Paper
• 1 Chapter
• 1 More
  • 1 Editorial

Research output per year

Research output per year

Search results

• 2022

  On an Multi-directional Searching Algorithm for Two Fuzzy Clustering Methods for Categorical Multivariate Data

  Suzuki, K. & Kanzawa, Y., 2022, Integrated Uncertainty in Knowledge Modelling and Decision Making - 9th International Symposium, IUKM 2022, Proceedings. Honda, K., Entani, T., Ubukata, S., Huynh, V-N. & Inuiguchi, M. (eds.). Springer Science and Business Media Deutschland GmbH, p. 182-190 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13199 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy Clustering 100%
  • Clustering Methods 92%
  • Fuzzy clustering 78%
  • Multivariate Data 76%
  • Nominal or categorical data 54%

• On Some Fuzzy Clustering Algorithms for Time-Series Data

  Fujita, M. & Kanzawa, Y., 2022, Integrated Uncertainty in Knowledge Modelling and Decision Making - 9th International Symposium, IUKM 2022, Proceedings. Honda, K., Entani, T., Ubukata, S., Huynh, V-N. & Inuiguchi, M. (eds.). Springer Science and Business Media Deutschland GmbH, p. 169-181 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13199 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy Algorithm 100%
  • Fuzzy Clustering 92%
  • Clustering Algorithm 83%
  • Fuzzy clustering 72%
  • Clustering algorithms 57%

• On Some Fuzzy Clustering Algorithms with Cluster-Wise Covariance

  Ishii, T. & Kanzawa, Y., 2022, Integrated Uncertainty in Knowledge Modelling and Decision Making - 9th International Symposium, IUKM 2022, Proceedings. Honda, K., Entani, T., Ubukata, S., Huynh, V-N. & Inuiguchi, M. (eds.). Springer Science and Business Media Deutschland GmbH, p. 191-203 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13199 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy Algorithm 100%
  • Fuzzy Clustering 92%
  • Clustering Algorithm 83%
  • Fuzzy clustering 72%
  • Fuzzy Classification 69%

• On Some Fuzzy Clustering Algorithms with Dimensionality Reduction

  Kawamura, M. & Kanzawa, Y., 2022, 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022. Institute of Electrical and Electronics Engineers Inc., (2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy Algorithm 100%
  • Fuzzy Clustering 92%
  • Clustering Algorithm 83%
  • Dimensionality Reduction 82%
  • Fuzzy clustering 72%
• 2021

  q-Divergence Regularization of Bezdek-Type Fuzzy Clustering for Categorical Multivariate Data

  Kanzawa, Y., 2021, Modeling Decisions for Artificial Intelligence - 18th International Conference, MDAI 2021, Proceedings. Torra, V. & Narukawa, Y. (eds.). Springer Science and Business Media Deutschland GmbH, p. 218-230 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12898 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy Clustering 100%
  • Nominal or categorical data 81%
  • Fuzzy clustering 78%
  • Multivariate Data 76%
  • Divergence 60%
• 2020

  Generalization property of fuzzy classification function for tsallis entropy-regularization of bezdek-type fuzzy c-means clustering

  Kanzawa, Y., 2020, Modeling Decisions for Artificial Intelligence - 17th International Conference, MDAI 2020, Proceedings. Torra, V., Narukawa, Y., Nin, J. & Agell, N. (eds.). Springer, p. 119-131 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12256 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy C-means Clustering 100%
  • Fuzzy Classification 97%
  • Tsallis Entropy 85%
  • Fuzzy clustering 67%
  • Clustering Methods 53%

• On fuzzy clustering algorithms for nominal data

  Kanzawa, Y., 2020 Dec 5, 2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020. Institute of Electrical and Electronics Engineers Inc., 9322791. (2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy Algorithm 100%
  • Fuzzy Clustering 92%
  • Clustering Algorithm 83%
  • Nominal or categorical data 75%
  • Fuzzy clustering 72%
• 2019

  On Collaborative Filtering with Possibilistic Clustering for Spherical Data Based on Tsallis Entropy

  Kanzawa, Y., 2019 Jan 1, Modeling Decisions for Artificial Intelligence - 16th International Conference, MDAI 2019, Proceedings. Torra, V., Narukawa, Y., Pasi, G. & Viviani, M. (eds.). Springer Verlag, p. 189-200 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11676 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Collaborative Filtering 100%
  • Fuzzy Clustering 85%
  • Tsallis Entropy 84%
  • Fuzzy clustering 67%
  • Collaborative filtering 66%

• On fuzzy clustering for incomplete spherical data and for incomplete multivariate categorical data

  Kanzawa, Y., 2019 May 15, Proceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018. Institute of Electrical and Electronics Engineers Inc., p. 638-643 6 p. 8716218. (Proceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy Clustering 100%
  • Nominal or categorical data 81%
  • Fuzzy clustering 78%
  • Multivariate Data 76%
  • Clustering algorithms 62%

• On Tsallis Entropy-Based and Bezdek-Type Fuzzy Latent Semantics Analysis

  Kanzawa, Y., 2019 Jan 16, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., p. 3685-3689 5 p. 8616620. (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Latent Semantic Analysis 100%
  • Latent Class Analysis 80%
  • Entropy 74%
  • Semantics 64%
  • Free energy 49%

• Performance comparison of collaborative filtering using fuzzy clustering for spherical data

  Kondo, T. & Kanzawa, Y., 2019 May 15, Proceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018. Institute of Electrical and Electronics Engineers Inc., p. 644-647 4 p. 8716226. (Proceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Collaborative Filtering 100%
  • Fuzzy Clustering 85%
  • Performance Comparison 85%
  • Fuzzy clustering 67%
  • Collaborative filtering 66%
  1 Citation (Scopus)
• 2018

  Generalized Fuzzy c-Means Clustering and Its Theoretical Properties

  Kanzawa, Y. & Miyamoto, S., 2018 Jan 1, Modeling Decisions for Artificial Intelligence - 15th International Conference, MDAI 2018, Proceedings. Torra, V., Torra, V., Narukawa, Y., González-Hidalgo, M. & Aguilo, I. (eds.). Springer Verlag, p. 243-254 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11144 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy C-means Clustering 100%
  • Derivatives 48%
  • Fuzzy Classification 32%
  • Fuzzy C-means 32%
  • Clustering algorithms 26%
  1 Citation (Scopus)
• 2017

  Comparison of fuzzy co-clustering methods in collaborative filtering-based recommender system

  Kondo, T. & Kanzawa, Y., 2017, Modeling Decisions for Artificial Intelligence - 14th International Conference, MDAI 2017, Proceedings. Honda, A., Narukawa, Y., Torra, V. & Inoue, S. (eds.). Springer Verlag, p. 103-116 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10571 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Collaborative Filtering 100%
  • Recommender Systems 97%
  • Clustering Methods 79%
  • Collaborative filtering 66%
  • Recommender systems 58%

• Fuzzy co-clustering induced by q-multinomial mixture models

  Kanzawa, Y., 2017 Aug 23, 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017. Institute of Electrical and Electronics Engineers Inc., 8015398. (IEEE International Conference on Fuzzy Systems).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Multinomial Model 100%
  • Mixture Model 78%
  • Clustering 69%
  • Pseudo-likelihood 38%
  • Multinomial Distribution 37%

• On fuzzy clustering for categorical multivariate data induced by polya mixture models

  Kanzawa, Y., 2017, Modeling Decisions for Artificial Intelligence - 14th International Conference, MDAI 2017, Proceedings. Springer Verlag, Vol. 10571 LNAI. p. 89-102 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10571 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy Clustering 100%
  • Nominal or categorical data 81%
  • Fuzzy clustering 78%
  • Multivariate Data 76%
  • Mixture Model 72%
  1 Citation (Scopus)
• 2016

  On Bezdek-type possibilistic clustering for spherical data, its kernelization, and spectral clustering approach

  Kanzawa, Y., 2016, Modeling Decisions for Artificial Intelligence - 13th International Conference, MDAI 2016, Proceedings. Springer Verlag, Vol. 9880 LNAI. p. 178-190 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9880 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Spectral Clustering 100%
  • Kernelization 88%
  • Clustering 63%
  • Principal component analysis 62%
  • Clustering algorithms 60%

• On Possibilistic Clustering Algorithms Based on Noise Clustering

  Kanzawa, Y., 2016 Dec 28, Proceedings - 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016. Institute of Electrical and Electronics Engineers Inc., p. 42-47 6 p. 7801610

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Clustering Methods 100%
  • Clustering Algorithm 97%
  • Clustering 70%
  • Clustering algorithms 66%
  • Entropy 62%
  1 Citation (Scopus)
• 2015

  Fuzzy clustering based on α-divergence for spherical data and for categorical multivariate data

  Kanzawa, Y., 2015 Nov 25, FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems. Yazici, A., Pal, N. R., Ishibuchi, H., Tutmez, B., Lin, C-T., Sousa, J. M. C., Kaymak, U. & Martin, T. (eds.). Institute of Electrical and Electronics Engineers Inc., 7337853. (IEEE International Conference on Fuzzy Systems; vol. 2015-November).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy Clustering 100%
  • Nominal or categorical data 81%
  • Fuzzy clustering 78%
  • Multivariate Data 76%
  • Clustering algorithms 62%
  7 Citations (Scopus)

• On possibilistic clustering methods based on Shannon/Tsallis-entropy for spherical data and categorical multivariate data

  Kanzawa, Y., 2015, Modeling Decisions for Artificial Intelligence - 12th International Conference, MDAI 2015, Proceedings. Torra, V. & Narukawa, Y. (eds.). Springer Verlag, p. 115-128 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9321).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Tsallis Entropy 100%
  • Clustering Methods 94%
  • Shannon Entropy 83%
  • Nominal or categorical data 82%
  • Multivariate Data 77%
  16 Citations (Scopus)
• 2014

  A maximizing model of Bezdek-like spherical fuzzy c-means clustering

  Kanzawa, Y., 2014 Sept 4, Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE. Institute of Electrical and Electronics Engineers Inc., p. 2482-2488 7 p. 6891670. (IEEE International Conference on Fuzzy Systems).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy C-means Clustering 100%
  • Fuzzy C-means 32%
  • Model 19%
  • Theoretical Analysis 18%
  • Regularization 17%
  4 Citations (Scopus)

• On a maximizing model of spherical Bezdek-type possibilistic c-means and fuzzy multi-medoids clustering

  Kanzawa, Y., 2014 Dec 11, Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014. Kudo, Y. & Tsumoto, S. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 121-126 6 p. 6982819. (Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy clustering 100%
  4 Citations (Scopus)

• On Bezdek-type fuzzy clustering for categorical multivariate data

  Kanzawa, Y., 2014 Feb 18, 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014. Institute of Electrical and Electronics Engineers Inc., p. 694-699 6 p. 7044511

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy clustering 100%
  • Entropy 20%
  • Clustering algorithms 7%
  4 Citations (Scopus)

• On kernelization for a maximizing model of bezdek-like spherical fuzzy c-means clustering

  Kanzawa, Y., 2014, Modeling Decisions forArtificial Intelligence - 11th International Conference, MDAI 2014, Proceedings. Torra, V., Narukawa, Y. & Endo, Y. (eds.). Springer Verlag, p. 108-121 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 8825).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy C-means Clustering 100%
  • Kernelization 78%
  • Spectral Clustering 44%
  • Object 20%
  • Model 19%
  10 Citations (Scopus)

• Relational fuzzy c-means and kernel fuzzy c-means using a quadratic programming-based object-wise β-spread transformation

  Kanzawa, Y., 2014, Knowledge and Systems Engineering - Proceedings of the 5th International Conference, KSE 2013. Denoeux, T., Huynh, V-N., Tran, D. H., Le, A. C. & Pham, S. B. (eds.). Springer Verlag, p. 29-43 15 p. (Advances in Intelligent Systems and Computing; vol. 245).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Quadratic programming 100%
• 2013

  Generalization of quadratic regularized and standard fuzzy c-means clustering with respect to regularization of hard c-means

  Kanzawa, Y., 2013 Dec 1, Modeling Decisions for Artificial Intelligence - 10th International Conference, MDAI 2013, Proceedings. p. 152-165 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 8234 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy C-means Clustering 100%
  • Clustering algorithms 53%
  • Regularization 51%
  • Standards 32%
  • Generalization 29%
  8 Citations (Scopus)
• 2012

  Comparison of imputation strategies in FNM-based and RFCM-based fuzzy co-clustering

  Kanzawa, Y., 2012 Dec 1, 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012. p. 1988-1993 6 p. 6505051. (6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Entropy 100%
  • Experiments 42%
  2 Citations (Scopus)

• FNM-based and RFCM-based fuzzy clustering for tri-relational data

  Kanzawa, Y., 2012 Dec 1, 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012. p. 1982-1987 6 p. 6505050. (6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy clustering 100%
  • Entropy 73%
  • Experiments 31%

• On FNM-based and RFCM-based fuzzy co-clustering algorithms

  Kanzawa, Y. & Endo, Y., 2012 Oct 23, 2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012. 6250781. (IEEE International Conference on Fuzzy Systems).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Clustering Algorithm 100%
  • Fuzzy Model 97%
  • Clustering algorithms 68%
  • Dissimilarity 56%
  • Fuzzy C-means 49%
  1 Citation (Scopus)

• Relational fuzzy c-lines derived from kernel fuzzy c-lines

  Kanzawa, Y., 2012 Dec 1, 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012. p. 1561-1566 6 p. 6505052. (6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy clustering 100%
  • Clustering algorithms 78%
• 2011

  On hard and fuzzy c-means clustering with conditionally positive definite kernel

  Kanzawa, Y., Endo, Y. & Miyamoto, S., 2011 Sept 27, FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings. p. 816-820 5 p. 6007431. (IEEE International Conference on Fuzzy Systems).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy C-means Clustering 100%
  • Positive Definite Kernels 83%
  • Feature Space 20%
  • Clustering algorithms 17%
  • Entropy 16%
• 2010

  Indefinite kernel fuzzy c-means clustering algorithms

  Kanzawa, Y., Endo, Y. & Miyamoto, S., 2010, Modeling Decisions for Artificial Intelligence - 7th International Conference, MDAI 2010, Proceedings. p. 116-128 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 6408 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy C-means Clustering 100%
  • Fuzzy C-means Algorithm 81%
  • Clustering Algorithm 78%
  • Clustering algorithms 53%
  • kernel 40%
  6 Citations (Scopus)
• 2009

  Entropy regularized fuzzy c-lines for data with tolerance

  Kanzawa, Y., Endo, Y. & Miyamoto, S., 2009, 2009 IEEE International Conference on Fuzzy Systems - Proceedings. p. 1113-1118 6 p. 5277176. (IEEE International Conference on Fuzzy Systems).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy Entropy 100%
  • Tolerance 71%
  • Clustering algorithms 61%
  • Entropy 56%
  • Karush-Kuhn-Tucker Conditions 46%

• Fuzzy c-lines for data with tolerance

  Kanzawa, Y., Endo, Y. & Miyamoto, S., 2009 Dec 1, 2009 International Fuzzy Systems Association World Congress and 2009 European Society for Fuzzy Logic and Technology Conference, IFSA-EUSFLAT 2009 - Proceedings. p. 861-866 6 p. (2009 International Fuzzy Systems Association World Congress and 2009 European Society for Fuzzy Logic and Technology Conference, IFSA-EUSFLAT 2009 - Proceedings).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Tolerance 100%
  • Clustering algorithms 85%
  • Karush-Kuhn-Tucker Conditions 64%
  • Line 63%
  • Clustering Algorithm 62%
  1 Citation (Scopus)

• On Fuzzy c-Means clustering for uncertain data using quadratic regularization of penalty vectors

  Endo, Y., Hamasuna, Y., Kanzawa, Y. & Miyamoto, S., 2009 Nov 25, 2009 IEEE International Conference on Granular Computing, GRC 2009. p. 148-153 6 p. 5255142. (2009 IEEE International Conference on Granular Computing, GRC 2009).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy C-means Clustering 100%
  • Uncertain Data 85%
  • Penalty 55%
  • Regularization 51%
  • Tolerance 31%
  1 Citation (Scopus)

• Some pairwise constrained semi-supervised fuzzy c-means clustering algorithms

  Kanzawa, Y., Endo, Y. & Miyamoto, S., 2009 Dec 1, Modeling Decisions for Artificial Intelligence - 6th International Conference, MDAI 2009, Proceedings. p. 268-281 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 5861 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy C-means Clustering 100%
  • Clustering Algorithm 78%
  • Fuzzy C-means 64%
  • Clustering algorithms 53%
  • Pairwise 52%
  6 Citations (Scopus)
• 2008

  Fuzzy classification function of entropy regularized fuzzy c-means algorithm for data with tolerance using kernel function

  Kanzawa, Y., Endo, Y. & Miyamoto, S., 2008 Dec 30, 2008 IEEE International Conference on Granular Computing, GRC 2008. p. 350-355 6 p. 4664765. (2008 IEEE International Conference on Granular Computing, GRC 2008).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Entropy 100%
  • Clustering algorithms 13%
  5 Citations (Scopus)

• Fuzzy classification function of fuzzy c-means algorithms for data with tolerance

  Kanzawa, Y., Endo, Y. & Miyamoto, S., 2008 Nov 7, 2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008. p. 1081-1088 8 p. 4630504. (IEEE International Conference on Fuzzy Systems).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy C-means Algorithm 100%
  • Fuzzy Classification 94%
  • Tolerance 60%
  • Fuzzy C-means 47%
  • Optimization Problem 31%

• Fuzzy classification function of standard fuzzy c-means algorithm for data with tolerance using kernel function

  Kanzawa, Y., Endo, Y. & Miyamoto, S., 2008 Dec 31, Modeling Decisions for Artificial Intelligence - 5th International Conference, MDAI 2008, Proceedings. p. 122-133 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 5285 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy C-means Algorithm 100%
  • Fuzzy Classification 94%
  • Kernel Function 61%
  • Tolerance 60%
  • Fuzzy C-means 47%

• Fuzzy c-means for data with tolerance by introducing penalty term

  Kanzawa, Y., Endo, Y. & Miyamoto, S., 2008 Dec 1, SMCia/08 - Proceedings of the 2008 IEEE Conference on Soft Computing on Industrial Applications. p. 371-376 6 p. 5045992. (SMCia/08 - Proceedings of the 2008 IEEE Conference on Soft Computing on Industrial Applications).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy C-means 100%
  • Tolerance 64%
  • Penalty 57%
  • Clustering Algorithm 40%
  • Term 33%
• 2006

  Fuzzy c-means clustering for data with tolerance using kernel functions

  Kanzawa, Y., Endo, Y. & Miyamoto, S., 2006 Dec 1, 2006 IEEE International Conference on Fuzzy Systems. p. 744-750 7 p. 1681793. (IEEE International Conference on Fuzzy Systems).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fuzzy C-means Clustering 100%
  • Feature Space 81%
  • Kernel Function 63%
  • Tolerance 62%
  • Clustering Algorithm 52%
  3 Citations (Scopus)