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ISBN 10: 6206750949 ISBN 13: 9786206750949
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Publicado por LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206750949 ISBN 13: 9786206750949
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Añadir al carritoTaschenbuch. Condición: Neu. Intelligent Techniques for Finding Frequent Patterns | from Large Database | Sheik Yousuf T. (u. a.) | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206750949 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206750949 ISBN 13: 9786206750949
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Publicado por LAP LAMBERT Academic Publishing Jul 2023, 2023
ISBN 10: 6206750949 ISBN 13: 9786206750949
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Frequent Pattern (FP) mining is a significant and well-researched technique of data mining. It is used to extract interesting patterns from large databases by applying association rules, classifier rules, correlation rules, clustering rules, and sequential rules. Many researchers have been highly concentrated on FP mining for the past years. This book reports on novel algorithms, for solving FP mining problems. A comparison of existing algorithms and their hybrid version is also reported for constantly changing databases. In addition, this work analyzes and studies various existing techniques for mining frequent item sets. This work also evaluates the performance of novel algorithms and compared them with classical techniques such as vertical-based layout, horizontal-based layout, and projected database algorithms. 140 pp. Englisch.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206750949 ISBN 13: 9786206750949
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Idioma: Inglés
Publicado por LAP Lambert Academic Publishing, 2023
ISBN 10: 6206750949 ISBN 13: 9786206750949
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Frequent Pattern (FP) mining is a significant and well-researched technique of data mining. It is used to extract interesting patterns from large databases by applying association rules, classifier rules, correlation rules, clustering rules, and sequential .
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Publicado por LAP LAMBERT Academic Publishing Jul 2023, 2023
ISBN 10: 6206750949 ISBN 13: 9786206750949
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Frequent Pattern (FP) mining is a significant and well-researched technique of data mining. It is used to extract interesting patterns from large databases by applying association rules, classifier rules, correlation rules, clustering rules, and sequential rules. Many researchers have been highly concentrated on FP mining for the past years. This book reports on novel algorithms, for solving FP mining problems. A comparison of existing algorithms and their hybrid version is also reported for constantly changing databases. In addition, this work analyzes and studies various existing techniques for mining frequent item sets. This work also evaluates the performance of novel algorithms and compared them with classical techniques such as vertical-based layout, horizontal-based layout, and projected database algorithms.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 140 pp. Englisch.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206750949 ISBN 13: 9786206750949
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Frequent Pattern (FP) mining is a significant and well-researched technique of data mining. It is used to extract interesting patterns from large databases by applying association rules, classifier rules, correlation rules, clustering rules, and sequential rules. Many researchers have been highly concentrated on FP mining for the past years. This book reports on novel algorithms, for solving FP mining problems. A comparison of existing algorithms and their hybrid version is also reported for constantly changing databases. In addition, this work analyzes and studies various existing techniques for mining frequent item sets. This work also evaluates the performance of novel algorithms and compared them with classical techniques such as vertical-based layout, horizontal-based layout, and projected database algorithms.