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Cited by 14
On a Minimal Spanning Tree Approach in the Cluster Validation Problem

2011 IEEE International Symposium on Intelligent Control
Oleg Granichin, Mikhail Morozkov, Zeev Volkovich
https://doi.org/10.1109/ISIC.2011.6045413
Conference  (2011), p. 1002
A learning automata-based heuristic algorithm for solving the minimum spanning tree problem in stochastic graphs
Javad Akbari Torkestani, Mohammad Reza Meybodi
https://doi.org/10.1007/s11227-010-0484-1
Journal  The Journal of Supercomputing Volume 59, Issue 2 (2012), p. 1035
A randomized algorithm for estimating the number of clusters
O. N. Granichin, D. S. Shalymov, R. Avros, Z. Volkovich
https://doi.org/10.1134/S0005117911040072
Journal  Automation and Remote Control Volume 72, Issue 4 (2011), p. 754
An Approach to Model Selection in Spectral Clustering with Application to the Writing Style Determination Problem
Renata Avros, Avi Soffer, Zeev Volkovich, Orly Yahalom
https://doi.org/10.1007/978-3-642-54105-6_2
Book  Communications in Computer and Information Science (Knowledge Discovery, Knowledge Engineering and Knowledge Management) Volume 415 (2013), p. 19
An application of the minimal spanning tree approach to the cluster stability problem
Z. Volkovich, Z. Barzily, G.-W. Weber, D. Toledano-Kitai, R. Avros
https://doi.org/10.1007/s10100-010-0157-4
Journal  Central European Journal of Operations Research Volume 20, Issue 1 (2012), p. 119
BLE Fingerprint Indoor Localization Algorithm Based on Eight-Neighborhood Template Matching
Mingfeng Li, Lichen Zhao, Ding Tan, Xiaozhe Tong
https://doi.org/10.3390/s19224859
Journal  Sensors Volume 19, Issue 22 (2019), p. 4859
How Many Clusters: A Validation Index for Arbitrary-Shaped Clusters
Ariel E. Baya, Pablo M. Granitto
https://doi.org/10.1109/TCBB.2013.32
Journal  IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 10, Issue 2 (2013), p. 401
Learning automata-based algorithms for solving stochastic minimum spanning tree problem
Javad Akbari Torkestani, Mohammad Reza Meybodi
https://doi.org/10.1016/j.asoc.2011.02.017
Journal  Applied Soft Computing Volume 11, Issue 6 (2011), p. 4064
New internal index for clustering validation based on graphs
J.C. Rojas-Thomas, M. Santos, M. Mora
https://doi.org/10.1016/j.eswa.2017.06.003
Journal  Expert Systems with Applications Volume 86 (2017), p. 334
On Application of a ProbabilisticK-Nearest Neighbors Model for Cluster Validation Problem
Zeev Volkovich, Zeev Barzily, Renata Avros, Dvora Toledano-Kitai
https://doi.org/10.1080/03610926.2011.562786
Journal  Communications in Statistics - Theory and Methods Volume 40, Issue 16 (2011), p. 2997
Randomized Algorithm of Finding the True Number of Clusters Based on Chebychev Polynomial Approximation
R. Avros, O. Granichin, D. Shalymov, Z. Volkovich, G. -W. Weber
https://doi.org/10.1007/978-3-642-23166-7_6
Book  Intelligent Systems Reference Library (Data Mining: Foundations and Intelligent Paradigms) Volume 23 (2012), p. 131
Regulatory Networks under Ellipsoidal Uncertainty – Data Analysis and Prediction by Optimization Theory and Dynamical Systems
Erik Kropat, Gerhard-Wilhelm Weber, Chandra Sekhar Pedamallu
https://doi.org/10.1007/978-3-642-23241-1_3
Book  Intelligent Systems Reference Library (Data Mining: Foundations and Intelligent Paradigms) Volume 24 (2012), p. 27
Resampling approach for cluster model selection
Z. Volkovich, Z. Barzily, G.-W. Weber, D. Toledano-Kitai, R. Avros
https://doi.org/10.1007/s10994-011-5236-9
Journal  Machine Learning Volume 85, Issue 1-2 (2011), p. 209
Self-learning K-means clustering: a global optimization approach
Z. Volkovich, D. Toledano-Kitai, G.-W. Weber
https://doi.org/10.1007/s10898-012-9854-y
Journal  Journal of Global Optimization Volume 56, Issue 2 (2013), p. 219


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