Hands-on Pattern Mining | Theory and Examples with PAMI, Sklearn, Keras, and TensorFlow

Uday Kiran Rage

ISBN 10: 9819667909 ISBN 13: 9789819667901
Verlag: Springer, 2025
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Hands-on Pattern Mining | Theory and Examples with PAMI, Sklearn, Keras, and TensorFlow | Uday Kiran Rage | Buch | xvii | Englisch | 2025 | Springer | EAN 9789819667901 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 133647331

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This book introduces pattern mining by presenting various pattern mining techniques and giving hands-on experience with each technique. Pattern mining is a popular data mining technique with many real-world applications, and involves discovering all user interest-based patterns that may exist in a database. Several models and numerous algorithms were described in the literature to find these patterns in binary databases, quantitative databases, uncertain databases, and streams. Since the lack of a Python toolkit containing these algorithms has limited the wide adaptability of pattern-mining techniques, the author developed Pattern Mining (PAMI) Python library, which currently contains 80+ algorithms to discover useful patterns in transactional databases, temporal databases, quantitative databases, and graphs.

The book consists of three main parts:

· Introduction: The first chapter introduces big data, types of learning techniques, and the importance of pattern mining. The second chapter introduces the PAMI library, its organizational structure, installation, and usage.

· Pattern mining algorithms and examples: The following chapters present the state-of-the-art techniques for discovering user interest-based patterns in (1) transactional databases, (2) temporal databases, (3) quantitative databases, (4) uncertain databases, (5) sequential databases, and (6) graphs.

· Applications: The book concludes with several applications, where the predicted knowledge using TensorFlow and PyTorch was transformed into a database to discover future trends or patterns.

Über die Autorin bzw. den Autor:

Rage Uday Kiran is an associate professor in the Division of Information Systems at The University of Aizu, Japan.

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Titel: Hands-on Pattern Mining | Theory and ...
Verlag: Springer
Erscheinungsdatum: 2025
Einband: Buch
Zustand: Neu

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Rage, Uday Kiran
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Buch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book introduces pattern mining by presenting various pattern mining techniques and giving hands-on experience with each technique. Pattern mining is a popular data mining technique with many real-world applications, and involves discovering all user interest-based patterns that may exist in a database. Several models and numerous algorithms were described in the literature to find these patterns in binary databases, quantitative databases, uncertain databases, and streams. Since the lack of a Python toolkit containing these algorithms has limited the wide adaptability of pattern-mining techniques, the author developed Pattern Mining (PAMI) Python library, which currently contains 80+ algorithms to discover useful patterns in transactional databases, temporal databases, quantitative databases, and graphs.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 200 pp. Englisch. Bestandsnummer des Verkäufers 9789819667901

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Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces pattern mining by presenting various pattern mining techniques and giving hands-on experience with each technique. Pattern mining is a popular data mining technique with many real-world applications, and involves discovering all user interest-based patterns that may exist in a database. Several models and numerous algorithms were described in the literature to find these patterns in binary databases, quantitative databases, uncertain databases, and streams. Since the lack of a Python toolkit containing these algorithms has limited the wide adaptability of pattern-mining techniques, the author developed Pattern Mining (PAMI) Python library, which currently contains 80+ algorithms to discover useful patterns in transactional databases, temporal databases, quantitative databases, and graphs.The book consists of three main parts: Introduction: The first chapter introduces big data, types of learning techniques, and the importance of pattern mining. The second chapter introduces the PAMI library, its organizational structure, installation, and usage. Pattern mining algorithms and examples: The following chapters present the state-of-the-art techniques for discovering user interest-based patterns in (1) transactional databases, (2) temporal databases, (3) quantitative databases, (4) uncertain databases, (5) sequential databases, and (6) graphs. 200 pp. Englisch. Bestandsnummer des Verkäufers 9789819667901

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Hardcover. Zustand: new. Hardcover. This book introduces pattern mining by presenting various pattern mining techniques and giving hands-on experience with each technique. Pattern mining is a popular data mining technique with many real-world applications, and involves discovering all user interest-based patterns that may exist in a database. Several models and numerous algorithms were described in the literature to find these patterns in binary databases, quantitative databases, uncertain databases, and streams. Since the lack of a Python toolkit containing these algorithms has limited the wide adaptability of pattern-mining techniques, the author developed Pattern Mining (PAMI) Python library, which currently contains 80+ algorithms to discover useful patterns in transactional databases, temporal databases, quantitative databases, and graphs.The book consists of three main parts: Introduction: The first chapter introduces big data, types of learning techniques, and the importance of pattern mining. The second chapter introduces the PAMI library, its organizational structure, installation, and usage. Pattern mining algorithms and examples: The following chapters present the state-of-the-art techniques for discovering user interest-based patterns in (1) transactional databases, (2) temporal databases, (3) quantitative databases, (4) uncertain databases, (5) sequential databases, and (6) graphs. Applications: The book concludes with several applications, where the predicted knowledge using TensorFlow and PyTorch was transformed into a database to discover future trends or patterns. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9789819667901

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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces pattern mining by presenting various pattern mining techniques and giving hands-on experience with each technique. Pattern mining is a popular data mining technique with many real-world applications, and involves discovering all user interest-based patterns that may exist in a database. Several models and numerous algorithms were described in the literature to find these patterns in binary databases, quantitative databases, uncertain databases, and streams. Since the lack of a Python toolkit containing these algorithms has limited the wide adaptability of pattern-mining techniques, the author developed Pattern Mining (PAMI) Python library, which currently contains 80+ algorithms to discover useful patterns in transactional databases, temporal databases, quantitative databases, and graphs.The book consists of three main parts: Introduction: The first chapter introduces big data, types of learning techniques, and the importance of pattern mining. The second chapter introduces the PAMI library, its organizational structure, installation, and usage. Pattern mining algorithms and examples: The following chapters present the state-of-the-art techniques for discovering user interest-based patterns in (1) transactional databases, (2) temporal databases, (3) quantitative databases, (4) uncertain databases, (5) sequential databases, and (6) graphs. Bestandsnummer des Verkäufers 9789819667901

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Hardcover. Zustand: new. Hardcover. This book introduces pattern mining by presenting various pattern mining techniques and giving hands-on experience with each technique. Pattern mining is a popular data mining technique with many real-world applications, and involves discovering all user interest-based patterns that may exist in a database. Several models and numerous algorithms were described in the literature to find these patterns in binary databases, quantitative databases, uncertain databases, and streams. Since the lack of a Python toolkit containing these algorithms has limited the wide adaptability of pattern-mining techniques, the author developed Pattern Mining (PAMI) Python library, which currently contains 80+ algorithms to discover useful patterns in transactional databases, temporal databases, quantitative databases, and graphs.The book consists of three main parts: Introduction: The first chapter introduces big data, types of learning techniques, and the importance of pattern mining. The second chapter introduces the PAMI library, its organizational structure, installation, and usage. Pattern mining algorithms and examples: The following chapters present the state-of-the-art techniques for discovering user interest-based patterns in (1) transactional databases, (2) temporal databases, (3) quantitative databases, (4) uncertain databases, (5) sequential databases, and (6) graphs. Applications: The book concludes with several applications, where the predicted knowledge using TensorFlow and PyTorch was transformed into a database to discover future trends or patterns. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9789819667901

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Hardcover. Zustand: new. Hardcover. This book introduces pattern mining by presenting various pattern mining techniques and giving hands-on experience with each technique. Pattern mining is a popular data mining technique with many real-world applications, and involves discovering all user interest-based patterns that may exist in a database. Several models and numerous algorithms were described in the literature to find these patterns in binary databases, quantitative databases, uncertain databases, and streams. Since the lack of a Python toolkit containing these algorithms has limited the wide adaptability of pattern-mining techniques, the author developed Pattern Mining (PAMI) Python library, which currently contains 80+ algorithms to discover useful patterns in transactional databases, temporal databases, quantitative databases, and graphs.The book consists of three main parts: Introduction: The first chapter introduces big data, types of learning techniques, and the importance of pattern mining. The second chapter introduces the PAMI library, its organizational structure, installation, and usage. Pattern mining algorithms and examples: The following chapters present the state-of-the-art techniques for discovering user interest-based patterns in (1) transactional databases, (2) temporal databases, (3) quantitative databases, (4) uncertain databases, (5) sequential databases, and (6) graphs. Applications: The book concludes with several applications, where the predicted knowledge using TensorFlow and PyTorch was transformed into a database to discover future trends or patterns. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9789819667901

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