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A Note on the Effectiveness of the Least Squares Consensus Clustering
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Mirkin B., Shestakoff A.
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.
Publication based on the results of:
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
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., , 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
Mirkin B., Nascimento S., Information Sciences 2012 No. 183 P. 16–34
An additive spectral method for fuzzy clustering is proposed. The method operates on a clustering model which is an extension of the spectral decomposition of a square matrix. The computation proceeds by extracting clusters one by one, which makes the spectral approach quite natural. The iterative extraction of clusters, also, allows us to draw several ...
Added: November 16, 2012