Editore: LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843359741 ISBN 13: 9783843359740
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 48,50
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Editore: LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843359741 ISBN 13: 9783843359740
Lingua: Inglese
Da: preigu, Osnabrück, Germania
EUR 50,45
Quantità: 5 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Frequent Pattern Mining in Transactional and Structured Databases | Different aspects of itemset, sequence and subtree discovery | Renáta Iváncsy | Taschenbuch | 144 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783843359740 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
Editore: LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843359741 ISBN 13: 9783843359740
Lingua: Inglese
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 128,98
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Editore: LAP LAMBERT Academic Publishing Okt 2010, 2010
ISBN 10: 3843359741 ISBN 13: 9783843359740
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 59,00
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data mining is a process of discovering hidden relationships in large amounts of data. Frequent pattern discovery is an important research area in the field of data mining. Its purpose is to find patterns which appear frequently in a large collection of data. This work deals with three main areas of frequent pattern mining, namely, frequent itemset, frequent sequence and frequent subtree discovery. Beside providing a brief overview of related works of each single frequent pattern mining problem mentioned before, the three theses offered in this work suggest novel methods for efficient discovery of the different types of frequent patterns. The new methods are compared to the best-known algorithms in the related fields. The performance analysis of the methods involves measurements of the execution time and memory requirements. 144 pp. Englisch.
Editore: LAP LAMBERT Academic Publishing Okt 2010, 2010
ISBN 10: 3843359741 ISBN 13: 9783843359740
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 59,00
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Data mining is a process of discovering hidden relationships in large amounts of data. Frequent pattern discovery is an important research area in the field of data mining. Its purpose is to find patterns which appear frequently in a large collection of data. This work deals with three main areas of frequent pattern mining, namely, frequent itemset, frequent sequence and frequent subtree discovery. Beside providing a brief overview of related works of each single frequent pattern mining problem mentioned before, the three theses offered in this work suggest novel methods for efficient discovery of the different types of frequent patterns. The new methods are compared to the best-known algorithms in the related fields. The performance analysis of the methods involves measurements of the execution time and memory requirements.Books on Demand GmbH, Überseering 33, 22297 Hamburg 144 pp. Englisch.
Editore: LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843359741 ISBN 13: 9783843359740
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 59,00
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data mining is a process of discovering hidden relationships in large amounts of data. Frequent pattern discovery is an important research area in the field of data mining. Its purpose is to find patterns which appear frequently in a large collection of data. This work deals with three main areas of frequent pattern mining, namely, frequent itemset, frequent sequence and frequent subtree discovery. Beside providing a brief overview of related works of each single frequent pattern mining problem mentioned before, the three theses offered in this work suggest novel methods for efficient discovery of the different types of frequent patterns. The new methods are compared to the best-known algorithms in the related fields. The performance analysis of the methods involves measurements of the execution time and memory requirements.