Preference-based Spatial Co-location Pattern Mining

The development of information technology has enabled the collection of large amounts of spatial data, which can be valuable for discovering implicit, non-trivial, and potentially valuable information. This book provides commercial software developers with proven and effective algorithms for detecting and filtering these implicit patterns, as well as a basis for further research in this promising field. Preference-based co-location pattern mining refers to mining constrained or condensed co-location patterns, and the book highlights techniques for solving a range of problems in this context, including maximal co-location pattern mining, closed co-location pattern mining, top-k co-location pattern mining, non-redundant co-location pattern mining, dominant co-location pattern mining, high utility co-location pattern mining, user-preferred co-location pattern mining, and similarity measures between spatial co-location patterns.

Format: Paperback / softback
Length: 294 pages
Publication date: 06 January 2023
Publisher: Springer Verlag, Singapore

The development of information technology has revolutionized the way we collect and analyze spatial data, enabling us to uncover hidden insights and valuable information that were previously inaccessible. Spatial co-location patterns, which refer to the spatial arrangement of objects or events, hold immense potential for various applications, ranging from urban planning to marketing. In this comprehensive book, commercial software developers are provided with proven and effective algorithms for detecting and filtering these implicit patterns from spatial data. The authors offer a detailed explanation of these algorithms, accompanied by easily implemented pseudocode, making it accessible to a wide range of readers.

Furthermore, the book serves as a valuable foundation for further research in this promising field. One of the key aspects of preference-based co-location pattern mining is the focus on mining constrained or condensed co-location patterns instead of mining all prevalent patterns. This approach is particularly useful in scenarios where the number of spatial features or objects is large, making it challenging to analyze all possible combinations. The book discusses various techniques for solving these problems, including maximal co-location pattern mining, closed co-location pattern mining, top-k co-location pattern mining, non-redundant co-location pattern mining, dominant co-location pattern mining, high utility co-location pattern mining, user-preferred co-location pattern mining, and similarity measures between spatial co-location patterns.

By presenting a systematic and mathematical study of preference-based spatial co-location pattern mining, this book caters to both novice readers who are new to the topic and experienced professionals seeking to expand their knowledge. The book is organized in a clear and concise manner, with each chapter covering a specific aspect of the field. It includes numerous examples and case studies to illustrate the practical applications of the discussed algorithms, making it an invaluable resource for practitioners.

In conclusion, the development of information technology has opened up a new realm of possibilities for spatial data analysis. Preference-based co-location pattern mining is a key technique that enables us to extract valuable information from spatial data, and this book provides a comprehensive guide for commercial software developers and researchers alike. By leveraging the power of spatial co-location patterns, we can gain a deeper understanding of the world around us and make informed decisions that can have a positive impact on society.

Weight: 480g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811675683
Edition number: 1st ed. 2022