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