Hands-on Pattern Mining : Theory and Examples with PAMI, Sklearn, Keras, and TensorFlow / by Uday Kiran Rage

Von:

Rage, Uday Kiran [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Verlag: Singapore : Springer Nature Singapore, 2025Verlag: Singapore : Imprint: Springer, 2025Auflage: 1st ed. 2025Beschreibung: 1 Online-Ressource(XVII, 182 p. 28 illus., 12 illus. in color.)ISBN:

9789819667918

Schlagwörter:

006.3 23

DOI: DOI: 10.1007/978-981-96-6791-8Online-Ressourcen:

Zugang im Netz des KIT

Zusammenfassung: Part I Fundamentals 1 Getting Started with PAMI: Introduction, Maintenance, and Usage -- 2 Handling Big Data: Classification, Storage, and Processing Techniques -- 3 Transactional Databases: Representation, Creation, and Statistics -- 4 Pattern Discovery in Transactional Databases -- 5 Temporal Databases: Representation, Creation, and Statistics -- 6 Pattern Discovery in Temporal Databases -- 7 Spatial Databases: Representation, Creation, and Statistics -- 8 Pattern Discovery in Spatial Databases -- 9 Utility Databases: Representation, Creation, and Statistics -- 10 Pattern Discovery in Utility Databases -- 11 Sequence Databases: Representation, Creation, and Statistics -- 12 Pattern Discovery in Sequence Databases -- Part II Advanced Concepts 13 Mining Symbolic Sequences -- 14 Pattern Discovery in Fuzzy Databases -- 15 Knowledge Discovery in Uncertain Databases -- 16 Finding Useful Patterns in Graph Databases -- Part III Applications 17 Discovering Air Pollution Patterns through the KDD Process -- 18 Discovering Futuristic Pollution Patterns Using Forecasting and Pattern Mining.Zusammenfassung: 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.PPN: PPN: 193050960XPackage identifier: Produktsigel: ZDB-2-SEB | ZDB-2-CWD | ZDB-2-SXPC

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