000 | 03474nam a22005415i 4500 | ||
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001 | 978-3-031-79210-6 | ||
003 | DE-He213 | ||
005 | 20240730165240.0 | ||
007 | cr nn 008mamaa | ||
008 | 220601s2022 sz | s |||| 0|eng d | ||
020 |
_a9783031792106 _9978-3-031-79210-6 |
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024 | 7 |
_a10.1007/978-3-031-79210-6 _2doi |
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050 | 4 | _aQA76.9.D343 | |
072 | 7 |
_aUNF _2bicssc |
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_aUYQE _2bicssc |
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_aCOM021030 _2bisacsh |
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_a006.312 _223 |
100 | 1 |
_aBonchi, Francesco. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _988051 |
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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. |
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300 |
_aXV, 133 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSynthesis Lectures on Data Mining and Knowledge Discovery, _x2151-0075 |
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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 |
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650 | 0 |
_aStatistics . _931616 |
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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 |
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700 | 1 |
_aGullo, Francesco. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _988057 |
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710 | 2 |
_aSpringerLink (Online service) _988060 |
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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 |
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856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-79210-6 |
912 | _aZDB-2-SXSC | ||
942 | _cEBK | ||
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_c86193 _d86193 |