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Prashanth Kumar Devarakonda (9 results)
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Clustering Categorical data: for exploratory data analysis
Language: English
Published by LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Condition: New.
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Clustering Categorical Data: For Exploratory Data Analysis
Language: English
Published by Lap Lambert Academic Publishing, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Paperback. Condition: Brand New. 52 pages. 8.66x5.91x0.12 inches. In Stock.
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Clustering Categorical data | for exploratory data analysis
Language: English
Published by LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Taschenbuch. Condition: 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.
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Clustering Categorical data
Language: English
Published by LAP LAMBERT Academic Publishing Sep 2012, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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.
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Clustering Categorical data: for exploratory data analysis
Language: English
Published by LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Condition: New. Print on Demand.
-
Clustering Categorical data: for exploratory data analysis
Language: English
Published by LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Condition: New. PRINT ON DEMAND.
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Clustering Categorical data
Language: English
Published by LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Condition: 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.
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Clustering Categorical data
Language: English
Published by LAP LAMBERT Academic Publishing Sep 2012, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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.
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Clustering Categorical data : for exploratory data analysis
Language: English
Published by LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659258601 ISBN 13: 9783659258602
Taschenbuch. Condition: 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.
