Clustering Categorical data: for exploratory data analysis - Couverture souple

Devarakonda, Prashanth Kumar; Nangunuri, Raghu

9783659258602: Clustering Categorical data: for exploratory data analysis

Couverture souple

ISBN 10 : 3659258601 ISBN 13 : 9783659258602

Editeur : LAP LAMBERT Academic Publishing, 2012

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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.

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� propos de l?auteur

Prashanth 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 of work experience in that area.

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�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2012
Langue: anglais
ISBN 10: 3659258601
ISBN 13: 9783659258602
Reliure: Broch�
Nombre de pages: 52
Coordonn�es du fabricant: non disponible
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Clustering Categorical data

Prashanth Kumar Devarakonda|Raghu Nangunuri

Edit� par LAP LAMBERT Academic Publishing, 2012

ISBN 10 : 3659258601 ISBN 13 : 9783659258602

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Etat : 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. N� de r�f. du vendeur 5143706

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Clustering Categorical data : for exploratory data analysis

Prashanth Kumar Devarakonda

Edit� par LAP LAMBERT Academic Publishing, 2012

ISBN 10 : 3659258601 ISBN 13 : 9783659258602

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783659258602

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Clustering Categorical data

Prashanth Kumar Devarakonda

Edit� par LAP LAMBERT Academic Publishing Sep 2012, 2012

ISBN 10 : 3659258601 ISBN 13 : 9783659258602

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783659258602

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Clustering Categorical data

Prashanth Kumar Devarakonda

Edit� par LAP LAMBERT Academic Publishing Sep 2012, 2012

ISBN 10 : 3659258601 ISBN 13 : 9783659258602

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Taschenbuch. Etat : Neu. 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.Books on Demand GmbH, �berseering 33, 22297 Hamburg 52 pp. Englisch. N� de r�f. du vendeur 9783659258602

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