Data Mining for Co-location Patterns: Principles and Applications

Co-location pattern mining is a data mining technique that identifies patterns from data and applies them in business intelligence and analytics. This book explains the fundamentals of co-location pattern mining, decision trees, and maximal instance co-location pattern mining, and their applications in pavement management, image classification, and geospatial buffer analysis.

Format: Hardback
Length: 210 pages
Publication date: 27 January 2022
Publisher: Taylor & Francis Ltd

Co-location pattern mining is a powerful technique for identifying sets of features that are frequently found in close proximity to each other. This book is dedicated to data mining for co-location patterns, a valid approach for extracting patterns from various types of data and applying them in business intelligence and analytics. It provides a comprehensive overview of the fundamentals of co-location pattern mining, including co-location decision trees and maximal instance co-location pattern mining. Additionally, it delves into data mining, machine learning, and statistics, providing readers with a step-by-step guide to understanding and applying these methods in pavement management, image classification, geospatial buffer analysis, and other fields.

The book is organized into chapters that follow a logical progression, making it easy for readers to grasp the techniques and their applications. Each chapter begins with an introduction that sets the stage for the topics to be covered, followed by detailed explanations and examples that illustrate the concepts in action. The text is accompanied by numerous figures and tables that help to clarify complex concepts and provide visual representations of the data and algorithms used.

One of the key strengths of this book is its emphasis on real-world applications. The authors have drawn upon their extensive experience in the field to provide case studies and examples that demonstrate the practical benefits of co-location pattern mining. These examples cover a wide range of industries, including transportation, healthcare, and finance, and showcase how co-location patterns can be used to improve decision-making, reduce costs, and enhance efficiency.

Another notable feature of the book is its comprehensive coverage of the latest research and developments in the field. The authors have kept up with the latest advancements in machine learning, statistics, and data mining techniques, and have incorporated them into their explanations and examples. This ensures that readers are equipped with the most up-to-date knowledge and tools to apply in their own work.

In conclusion, this book is an essential resource for anyone interested in data mining for co-location patterns. It provides a comprehensive and practical guide to the fundamentals and applications of this technique, making it accessible to a wide range of readers, from beginners to experienced professionals. Whether you are working in industry or academia, this book will help you unlock the hidden insights in your data and make informed decisions that drive success.

Weight: 532g
Dimension: 224 x 243 x 22 (mm)
ISBN-13: 9780367654269