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001 978-3-031-79210-6
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020 _a9783031792106
_9978-3-031-79210-6
024 7 _a10.1007/978-3-031-79210-6
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
100 1 _aBonchi, Francesco.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_988051
245 1 0 _aCorrelation Clustering
_h[electronic resource] /
_cby Francesco Bonchi, David García-Soriano, Francesco Gullo.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXV, 133 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aData mining.
_93907
650 0 _aStatistics .
_931616
650 1 4 _aData Mining and Knowledge Discovery.
_988055
650 2 4 _aStatistics.
_914134
700 1 _aGarcía-Soriano, David.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_988056
700 1 _aGullo, Francesco.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_988057
710 2 _aSpringerLink (Online service)
_988060
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031792229
776 0 8 _iPrinted edition:
_z9783031791987
776 0 8 _iPrinted edition:
_z9783031792342
830 0 _aSynthesis Lectures on Data Mining and Knowledge Discovery,
_x2151-0075
_988061
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79210-6
912 _aZDB-2-SXSC
942 _cEBK
999 _c86193
_d86193