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008			230504s2022 sz | o |||| 0|eng d
020			_a9783031792106
024	7		_a10.1007/978-3-031-79210-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA278 _b2022 EB
100	1		_aBonchi, Francesco _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688344
245	1	0	_aCorrelation Clustering _cby Bonchi Francesco, García-Soriano David, Gullo Francesco
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XV, 133 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Data Mining and Knowledge Discovery _x2151-0075
505	0		_aPreface -- Acknowledgments -- Foundations -- Constraints -- Relaxed Formulations -- Other Types of Graphs -- Other Computational Settings -- Conclusions and Open Problems -- Bibliography -- Authors' Biographies.
520			_aGiven a set of objects and a pairwise similarity measure between them, the goal of correlation clustering is to partition the objects in a set of clusters to maximize the similarity of the objects within the same cluster and minimize the similarity of the objects in different clusters. In most of the variants of correlation clustering, the number of clusters is not a given parameter; instead, the optimal number of clusters is automatically determined. Correlation clustering is perhaps the most natural formulation of clustering: as it just needs a definition of similarity, its broad generality makes it applicable to a wide range of problems in different contexts, and, particularly, makes it naturally suitable to clustering structured objects for which feature vectors can be difficult to obtain. Despite its simplicity, generality, and wide applicability, correlation clustering has so far received much more attention from an algorithmic-theory perspective than from the data-mining community. The goal of this lecture is to show how correlation clustering can be a powerful addition to the toolkit of a data-mining researcher and practitioner, and to encourage further research in the area.
988			_aSynthesis Collection of Technology_2022
650		7	_2embne _9669583 _aAnálisis Cluster
650		7	_2embne _9162648 _aData mining
700	1		_aGarcía-Soriano, David _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688345
700	1		_aGullo, Francesco _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688346
776	0	8	_iPrinted edition: _z9783031792229
776	0	8	_iPrinted edition: _z9783031791987
776	0	8	_iPrinted edition: _z9783031792342
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-79210-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _da _eIG _zSI