Process Mining Techniques for Pattern Recognition : Concepts, Theory, and Practice

Title: Process Mining Techniques for Pattern Recognition : Concepts, Theory, and Practice
Author: Anil Kumar Dubey, Erma Suryani, Gaurav Dubey, Harivans Pratap Singh, Vikash Yadav
ISBN: 0367770490 / 9780367770495
Format: Hard Cover
Pages: 180
Publisher: CRC Press
Year: 2022
Availability: 2 to 3 weeks

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This book focuses on the theory, practice, and concepts of process mining techniques in detail, especially pattern recognition in diverse society, science, medicine, engineering, and business. The book deliberates several perspectives on process mining techniques in the broader context of data science and big data approaches.%u3000

Process Mining Techniques for Pattern Recognition: Concepts, Theory, and Practice provides an introduction to process mining techniques and pattern recognition. After that, it delivers the fundamentals of process modelling and mining essential to comprehend the book. The text emphasizes discovery as an important process mining task and includes case studies as well as real-life examples to guide users in successfully applying process mining techniques for pattern recognition in practice.

Intended to be an introduction to process mining and pattern recognition for students, academics, and practitioners, this book is perfect for those who want to learn the basics, and also gain an understanding of the concepts on a deeper level.

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Preface

Part I : Introduction to Process Mining Techniques
Chapter 1 :
Concepts of Data Mining and Process Mining
Chapter 2 : Process Mining Techniques
Chapter 3 : Quality Criteria

Part II : Difficulties to Apply Process Mining in Practice
Chapter 4 :
Applying Process Mining Techniques to Different Tasks
Chapter 5 : Process Mining Techniques : Issues

Part III : Process Mining Techniques for Pattern Recognition
Chapter 6 :
Pattern Recognition
Chapter 7 : Role of Process Mining Techniques for Pattern Recognition
Chapter 8 : Emerging Application and Research Trends
Chapter 9 : Future Challenge in Pattern Recognition
Chapter 10 : Case Study

Index