Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Da: Books Puddle, New York, NY, U.S.A.
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Lingua: Inglese
Editore: Lap Lambert Academic Publishing, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
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Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Clustering Categorical data | for exploratory data analysis | Prashanth Kumar Devarakonda (u. a.) | Taschenbuch | 52 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659258602 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Sep 2012, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data clustering is an important technique for exploratory data analysis and has been the focus of substantial research in several domains for decades among which Sampling has been recognized as an important technique to improve the efficiency of clustering. However, with sampling applied, those points that are not sampled will not have their labels after the normal process. Although there is a straightforward approach in the numerical domain, the problem of how to allocate those unlabeled data points into proper clusters remains as a challenging issue in the categorical domain. 52 pp. Englisch.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Da: Majestic Books, Hounslow, Regno Unito
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Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Da: moluna, Greven, Germania
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Devarakonda Prashanth KumarPrashanth Kumar Devarakonda completed both Masters and Bachelors program in Computer Science and Engineering in 2010 and 2005 respectively. Keen interests are in the field of Data Mining and has Eight years.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Sep 2012, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Data clustering is an important technique for exploratory data analysis and has been the focus of substantial research in several domains for decades among which Sampling has been recognized as an important technique to improve the efficiency of clustering. However, with sampling applied, those points that are not sampled will not have their labels after the normal process. Although there is a straightforward approach in the numerical domain, the problem of how to allocate those unlabeled data points into proper clusters remains as a challenging issue in the categorical domain.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 49,00
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data clustering is an important technique for exploratory data analysis and has been the focus of substantial research in several domains for decades among which Sampling has been recognized as an important technique to improve the efficiency of clustering. However, with sampling applied, those points that are not sampled will not have their labels after the normal process. Although there is a straightforward approach in the numerical domain, the problem of how to allocate those unlabeled data points into proper clusters remains as a challenging issue in the categorical domain.