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.

Presents ample examples to illustrate the theoretical concepts Provides Python codes to implement the concepts Makes use of open-source software and real-world databases for practical purposes

Autorentext

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

Inhalt

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.