Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by Crossref.
Diday, E.
1984.
Compstat 1984.
p.
119.
Diday, E
and
Moreau, J.V
1984.
Learning hierarchical clustering from examples — application to the adaptive construction of dissimilarity indices.
Pattern Recognition Letters,
Vol. 2,
Issue. 6,
p.
365.
Day, William H. E.
and
Edelsbrunner, Herbert
1984.
Efficient algorithms for agglomerative hierarchical clustering methods.
Journal of Classification,
Vol. 1,
Issue. 1,
p.
7.
Day, William H. E.
and
Edelsbrunner, Herbert
1985.
Investigation of proportional link linkage clustering methods.
Journal of Classification,
Vol. 2,
Issue. 1,
p.
239.
Podani, János
1989.
Numerical syntaxonomy.
p.
61.
Milligan, Glenn W.
1989.
A validation study of a variable weighting algorithm for cluster analysis.
Journal of Classification,
Vol. 6,
Issue. 1,
p.
53.
Podani, J�nos
1989.
New combinatorial clustering methods.
Vegetatio,
Vol. 81,
Issue. 1-2,
p.
61.
Vach, Werner
and
Degens, Paul O.
1991.
A new approach to isotonic agglomerative hierarchical clustering.
Journal of Classification,
Vol. 8,
Issue. 2,
p.
217.
Jolliffe, I. T.
Jones, B.
and
Morgan, B. J. T.
1995.
Identifying influential observations in hierarchical cluster analysis.
Journal of Applied Statistics,
Vol. 22,
Issue. 1,
p.
61.
Takeuchi, Akinobu
Yadohisa, Hiroshi
and
Inada, Koichi
2001.
Space Distortion and Monotone Admissibility in Agglomerative Clustering.
Behaviormetrika,
Vol. 28,
Issue. 2,
p.
153.
Rivera-Borroto, Oscar Miguel
Marrero-Ponce, Yovani
García-de la Vega, José Manuel
and
Grau-Ábalo, Ricardo del Corazón
2011.
Comparison of Combinatorial Clustering Methods on Pharmacological Data Sets Represented by Machine Learning-Selected Real Molecular Descriptors.
Journal of Chemical Information and Modeling,
Vol. 51,
Issue. 12,
p.
3036.
Korenjak‐Černe, Simona
Batagelj, Vladimir
and
Japelj Pavešić, Barbara
2011.
Clustering large data sets described with discrete distributions and its application on TIMSS data set.
Statistical Analysis and Data Mining: The ASA Data Science Journal,
Vol. 4,
Issue. 2,
p.
199.
Bank, Mathias
and
Schwenker, Friedhelm
2012.
Challenges at the Interface of Data Analysis, Computer Science, and Optimization.
p.
3.
Inaba, Daiki
Fukui, Ken-ichi
Sato, Kazuhisa
Mizusaki, Junichirou
and
Numao, Masayuki
2012.
Mining of Co-occurring Clusters for Damage Pattern Extraction of a Fuel Cell.
Transactions of the Japanese Society for Artificial Intelligence,
Vol. 27,
Issue. 3,
p.
121.
Górecki, Jan
Hofert, Marius
and
Holeňa, Martin
2017.
On structure, family and parameter estimation of hierarchical Archimedean copulas.
Journal of Statistical Computation and Simulation,
Vol. 87,
Issue. 17,
p.
3261.
Batagelj, Vladimir
2019.
Advances in Network Clustering and Blockmodeling.
p.
65.
Vijaya Prabhagar, M.
and
Punniyamoorthy, M.
2020.
Development of new agglomerative and performance evaluation models for classification.
Neural Computing and Applications,
Vol. 32,
Issue. 7,
p.
2589.
Górecki, Jan
Hofert, Marius
and
Okhrin, Ostap
2021.
Outer power transformations of hierarchical Archimedean copulas: Construction, sampling and estimation.
Computational Statistics & Data Analysis,
Vol. 155,
Issue. ,
p.
107109.
De Luca, Giovanni
and
Zuccolotto, Paola
2021.
Hierarchical time series clustering on tail dependence with linkage based on a multivariate copula approach.
International Journal of Approximate Reasoning,
Vol. 139,
Issue. ,
p.
88.
Randriamihamison, Nathanaël
Vialaneix, Nathalie
and
Neuvial, Pierre
2021.
Applicability and Interpretability of Ward’s Hierarchical Agglomerative Clustering With or Without Contiguity Constraints.
Journal of Classification,
Vol. 38,
Issue. 2,
p.
363.