 Название: Hands-on Pattern Mining: Theory and Examples with PAMI, Sklearn, Keras, and TensorFlow
Автор: Uday Kiran Rage
Издательство: Springer
Год: 2025
Страниц: 182
Язык: английский
Формат: pdf (true), epub
Размер: 34.4 MB
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.
Pattern mining is essential for uncovering valuable patterns hidden in big data. While software such as WEKA, Mahout, SPMF, and Knime offer some capabilities, they are often limited in algorithms or integration. To overcome these limitations, researchers at the University of Aizu have developed the pattern mining (PAMI) package. This open-source Python package, available on GitHub and distributed through the Python Package Index, offers over 80 algorithms to identify user interest-based patterns in various databases across multiple computing environments. This chapter introduces the architecture and systematic organization of the algorithms in PAMI. It provides detailed guidance on the installation, maintenance, and execution of the algorithms in PAMI, both from the terminal and within Python programs. Additionally, the chapter explains the input and output requirements for the algorithms, including how they report runtime and memory usage. Through practical examples and instructions, this chapter aims to help users effectively utilize the PAMI package for pattern mining tasks.
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.
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Hands-on Pattern Mining: Theory and Examples with PAMI, Sklearn, Keras, and TensorFlow 
