Crisp and fuzzy k-means clustering algorithms for multivariate functional data S Tokushige, H Yadohisa, K Inada Computational Statistics 22, 1-16, 2007 | 106 | 2007 |
Data analysis of asymmetric structures: advanced approaches in computational statistics T Saito, H Yadohisa CRC Press, 2004 | 71 | 2004 |
Asymmetric agglomerative hierarchical clustering algorithms and their evaluations A Takeuchi, T Saito, H Yadohisa Journal of Classification 24 (1), 123-143, 2007 | 34 | 2007 |
Supply chain management and organizational performance: the resonant influence BAT Duong, HQ Truong, M Sameiro, P Sampaio, AC Fernandes, ... International Journal of Quality & Reliability Management 36 (7), 1053-1077, 2019 | 28 | 2019 |
Software development productivity of Japanese enterprise applications M Tsunoda, A Monden, H Yadohisa, N Kikuchi, K Matsumoto Information Technology and Management 10, 193-205, 2009 | 28 | 2009 |
Effect of Data Standardization on the Result of k-Means Clustering K Tanioka, H Yadohisa Challenges at the Interface of Data Analysis, Computer Science, and …, 2012 | 26 | 2012 |
Reduced -means clustering with MCA in a low-dimensional space M Mitsuhiro, H Yadohisa Computational Statistics 30 (2), 463-475, 2015 | 17 | 2015 |
Data-oriented learning system of statistics based on analysis scenario/story (DoLStat) Y Mori, Y Yamamoto, H Yadohisa Bulletin of the International Statistical Institute, 54th Session …, 2003 | 16 | 2003 |
Non-hierarchical clustering for distribution-valued data Y Terada, H Yadohisa Proceedings of COMPSTAT, 1653-1660, 2010 | 14 | 2010 |
Revealing changes in brain functional networks caused by focused-attention meditation using Tucker3 clustering T Miyoshi, K Tanioka, S Yamamoto, H Yadohisa, T Hiroyasu, S Hiwa Frontiers in Human Neuroscience 13, 473, 2020 | 13 | 2020 |
Productivity analysis of Japanese enterprise software development projects M Tsunoda, A Monden, H Yadohisa, N Kikuchi, K Matsumoto Proceedings of the 2006 international workshop on Mining software …, 2006 | 13 | 2006 |
Formulation of asymmetric agglomerative hierarchical clustering and graphical representation of its results H Yadohisa Bulletin of the Computational Statistics of Japan,(15), 309-316, 2002 | 13 | 2002 |
8. Functional Data Analysis DISSIMILARITY AND RELATED METHODS FOR FUNCTIONAL DATA S Tokushige, K Inada, H Yadohisa Journal of the Japanese Society of Computational Statistics 15 (2), 319-326, 2003 | 10 | 2003 |
Clustering preference data in the presence of response‐style bias M Takagishi, M van de Velden, H Yadohisa British Journal of Mathematical and Statistical Psychology 72 (3), 401-425, 2019 | 9 | 2019 |
A non-negative matrix factorization model based on the zero-inflated Tweedie distribution H Abe, H Yadohisa Computational Statistics 32 (2), 475-499, 2017 | 9 | 2017 |
Developing Criteria for Measuring Space Distortion in Combinatorial Cluster Analysis and Methods for Controlling the Distortion. H Yadohisa, A Takeuchi, K Inada Journal of Classification 16 (1), 1999 | 9 | 1999 |
An estimation of causal structure based on Latent LiNGAM for mixed data M Yamayoshi, J Tsuchida, H Yadohisa Behaviormetrika 47, 105-121, 2020 | 8 | 2020 |
Correspondence analysis for symbolic contingency tables based on interval algebra I Takagi, H Yadohisa Procedia Computer Science 6, 352-357, 2011 | 7 | 2011 |
Vector field representation of asymmetric proximity data H Yadohisa, N Niki Communications in Statistics-Theory and Methods 28 (1), 35-48, 1999 | 7 | 1999 |
Orthogonal nonnegative matrix tri-factorization based on Tweedie distributions H Abe, H Yadohisa Advances in Data Analysis and Classification 13 (4), 825-853, 2019 | 6 | 2019 |