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		<identifier>oai:zbc.uz.zgora.pl:85906</identifier>
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<dc:title xml:lang="pl"><![CDATA[Clustering based on eigenvectors of the adjacency matrix]]></dc:title>
<dc:creator><![CDATA[Lucińska, Małgorzata]]></dc:creator>
<dc:creator><![CDATA[Wierzchoń, Sławomir T.]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[spectral clustering]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[adjacency matrix eigenvalues/eigenvectors]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[graph perturbation theory]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[eigengap heuristics]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[The paper presents a novel spectral algorithm EVSA (eigenvector structure analysis), which uses eigenvalues and eigenvectors of the adjacency matrix in order to discover clusters. Based on matrix perturbation theory and properties of graph spectra we show that the adjacency matrix can be more suitable for partitioning than other Laplacian matrices. The main problem concerning the use of the adjacency matrix is the selection of the appropriate eigenvectors.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[We thus propose an approach based on analysis of the adjacency matrix spectrum and eigenvector pairwise correlations. Formulated rules and heuristics allow choosing the right eigenvectors representing clusters, i.e., automatically establishing the number of groups. The algorithm requires only one parameter - the number of nearest neighbors. Unlike many other spectral methods, our solution does not need an additional clustering algorithm for final partitioning. We evaluate the proposed approach using real-world datasets of different sizes. Its performance is competitive to other both standard and new solutions, which require the number of clusters to be given as an input parameter.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Korbicz, Józef (1951- ) - red.]]></dc:contributor>
<dc:contributor><![CDATA[Uciński, Dariusz - red.]]></dc:contributor>
<dc:date><![CDATA[2018]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/85906/AMCS_2018_28_4_13.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/100898/edition/85906/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:85906]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 28, number 4 (2018)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=110]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:100898]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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