Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Jun 2021, 2021
ISBN 10: 6203855723 ISBN 13: 9786203855722
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -Attribute clustering is one of the unsupervised data mining applications which have been previously used to identify statistical dependence between subsets of variables. Again clustering techniques are important in data mining methods for exploring natural structure and identifying interesting patterns in original data, also it is proved to be helpful in finding co-expressed samples. In this book, the rough set theory (RST) has been used for attribute clustering. RST is a theory adopted to deal with rough and unsure knowledge, which analyzes the clusters and finds the data principles when previous knowledge is not available. In this concern, after implementing the rough set based attribute clustering method on a real life dataset, those are classified using some of the traditional classification techniques.Books on Demand GmbH, Überseering 33, 22297 Hamburg 96 pp. Englisch.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203855723 ISBN 13: 9786203855722
Da: preigu, Osnabrück, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Supervised and Unsupervised Learning for Genetic Expression | Rudra Kalyan Nayak (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786203855722 | 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 Jun 2021, 2021
ISBN 10: 6203855723 ISBN 13: 9786203855722
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 54,90
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Attribute clustering is one of the unsupervised data mining applications which have been previously used to identify statistical dependence between subsets of variables. Again clustering techniques are important in data mining methods for exploring natural structure and identifying interesting patterns in original data, also it is proved to be helpful in finding co-expressed samples. In this book, the rough set theory (RST) has been used for attribute clustering. RST is a theory adopted to deal with rough and unsure knowledge, which analyzes the clusters and finds the data principles when previous knowledge is not available. In this concern, after implementing the rough set based attribute clustering method on a real life dataset, those are classified using some of the traditional classification techniques. 96 pp. Englisch.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203855723 ISBN 13: 9786203855722
Da: moluna, Greven, Germania
EUR 45,45
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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: Kalyan Nayak RudraDr. Rudra Kalyan Nayak is presently working as Associate Professor in the Dept. of CSE at K L University, Andhra Pradesh, IndiaDr. Ramamani Tripathy is presently working as Associate Professor in the Dept. of MCA at.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203855723 ISBN 13: 9786203855722
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 55,56
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Attribute clustering is one of the unsupervised data mining applications which have been previously used to identify statistical dependence between subsets of variables. Again clustering techniques are important in data mining methods for exploring natural structure and identifying interesting patterns in original data, also it is proved to be helpful in finding co-expressed samples. In this book, the rough set theory (RST) has been used for attribute clustering. RST is a theory adopted to deal with rough and unsure knowledge, which analyzes the clusters and finds the data principles when previous knowledge is not available. In this concern, after implementing the rough set based attribute clustering method on a real life dataset, those are classified using some of the traditional classification techniques.