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Least-squares consensus clustering versus: (a) other consensus approaches and (b) k-means
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Mirkin B., Andrey Shestakov
We develop a consensus clustering framework proposed three decades ago in Russia and experimentally demonstrate that our least squares consensus clustering algorithm consistently outperforms several recent consensus clustering methods.
In book
Vol. 92. , Berlin: Springer, 2014.
Mirkin B., Parinov A., Автоматика и телемеханика 2024 № 3 С. 6–22
This paper reports of theoretical and computational results related to an original concept of consensus clustering involving what we call the projective distance between partitions. This distance is defined as the squared difference between a partition incidence matrix and its image over the orthogonal projection in the linear space spanning the other partition incidence matrix. ...
Added: February 24, 2025
Kurmukov A., Mussabaeva A., Denisova Y. et al., Brain Connectivity 2020 Vol. 10 No. 4 P. 183–194
This work addresses the problem of constructing a unified, topologically optimal connectivity-based brain atlas. The proposed approach aggregates an ensemble partition from individual parcellations without label agreement, providing a balance between sufficiently flexible individual parcellations and intuitive representation of the average topological structure of the connectome. The methods exploit a previously proposed dense connectivity representation, ...
Added: September 16, 2020
Bulgakov S. A., В кн.: Современные проблемы математического моделирования, обработки изображений и параллельных вычисленийТ. 2.: Ростов н/Д: ООО "ДГТУ-Принт", 2017. С. 36–44.
The paper covers mathematical and heuristic approaches for solution the image restoration problem. Attention is paid to the least squares method, least absolute deviations, Tikhonov regularization, total variation, Wiener and Kalman filters, as well as matched filter. A description of a new method for constructing the maximum likelihood estimate is given. Such heuristic approaches as ...
Added: September 24, 2018
Бочаров А. А., Gnatyshak D. V., Ignatov D. I. et al., , in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop ProceedingsVol. 1624.: M.: Higher School of Economics, National Research University, 2016. P. 45–56.
We propose a new algorithm for consensus clustering, FCA-Consensus, based on Formal Concept Analysis. As the input, the algorithm takes T partitions of a certain set of objects obtained by k-means algorithm after T runs from different initialisations. The resulting consensus partition is extracted from an antichain of the concept lattice built on a formal ...
Added: October 24, 2016
Mirkin B., Shestakoff A., , in: Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th BirthdayVol. 92.: Berlin: Springer, 2014.
We develop a consensus clustering framework proposed three decades ago in Russia and experimentally demonstrate that our least squares consensus clustering algorithm consistently outperforms several recent consensus clustering methods. ...
Added: January 23, 2015
Mirkin B., , in: Models, Algorithms, and Technologies for Network AnalysisVol. 59.: NY: Springer, 2013. P. 101–126.
There exists much prejudice against the within-cluster summary similarity criterion which supposedly leads to collecting all the entities in one cluster. This is not so if the similarity matrix is pre-processed by subtraction of ``noise'', of which two ways, the uniform and modularity, are mentioned in the paper. Another criterion under consideration is the semi-average ...
Added: November 22, 2013
Mirkin B., , in: Rough Sets, Fuzzy Sets, Data Mining, and Granular ComputingIssue 8170: Lecture Notes in Artificial Intelligence.: Heidelberg: Springer, 2013. P. 26–37.
A least-squares data approximation approach to finding individual clusters is advocated. A simple local optimization algorithm leads to suboptimal clusters satisfying some natural tightness criteria. Three versions of an iterative extraction approach are considered, leading to a portrayal of the cluster structure of the data. Of these, probably most promising is what is referred to ...
Added: October 29, 2013
Mirkin B., Shestakoff A., , in: Advances in Information Retrieval.: L.: Springer, 2013. P. 764–768.
We develop a consensus clustering framework developed three decades ago in Russia and experimentally demonstrate that our least squares consensus clustering algorithm consistently outperforms several recent consensus clustering methods. ...
Added: April 15, 2013