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July 2, 2026
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Individual approximate clusters: methods, properties, applications

P. 26–37.
Mirkin B.

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 as the incjunctive clustering approach. Applications are considered to the analysis of semantics, to integrating different knowledge aspects and consensus clustering.

Language: English
Full text
Keywords: consensus clusteringdata recovery approachsingle clusterkernel cluster
Publication based on the results of:
Методы визуализации текстовой информации с помощью построения суффиксных деревьев, мультифасетных классификаций и иерархических онтологий: алгоритмическое и программное обеспечение (2013)

In book

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
Issue 8170: Lecture Notes in Artificial Intelligence. , Heidelberg: Springer, 2013.
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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. ...
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A comprehensive approach is presented to analyse season's coastal upwelling represented by weekly sea surface temperature (SST) image grids. Our three-stage data recovery clustering method assumes that the season's upwelling can be divided into shorter periods of stability, ranges, each to be represented by a constant core and variable shell parts. Corresponding clustering algorithms parameters ...
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Added: November 14, 2020
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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 ...
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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
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