Zhou G. Data Mining for Co-location Patterns: Principles and Applications

CRC Press, Taylor & Francis Group, 2022. — 229 p. — ISBN: 978-0-367-65426-9.Co-location pattern mining detects sets of features frequently located close to each other. This book focuses on data mining for co-location patterns, a valid method for identifying patterns from all types of data and applying them in business intelligence and analytics. It explains the fundamentals of co-location pattern mining, co-location decision tree, and maximal instance co-location pattern mining along with an in-depth overview of data mining, machine learning, and statistics. This arrangement of chapters helps readers understand the methods of co-location pattern mining step-by-step and their applications in pavement management, image classification, geospatial buffer analysis, etc.Fundamentals of Mining Co-Location Patterns
Principle of Mining Co-Location Patterns
Manifold Learning Co-Location Pattern Mining
Maximal Instance Co-Location Pattern Mining Algorithms
Negative Co-Location Pattern Mining Algorithms
Application of Mining Co-Location Patterns in Pavement Management and Rehabilitation
Application of Mining Co-Location Patterns in Buffer Analysis
Application of Mining Co-Location Patterns in Remotely Sensed Imagery Classification