Preference-based Spatial Co-location Pattern Mining

Language: English

Published by Springer Nature Singapore, 2023

9811675686 / 9789811675683

Series: Book 4 of 13 - Big Data Management

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Zustand: Hervorragend | Seiten: 312 | Sprache: Englisch | Produktart: Bücher | The development of information technology has made it possible to collect large amounts of spatial data on a daily basis. It is of enormous significance when it comes to discovering implicit, non-trivial and potentially valuable information from this spatial data. Spatial co-location patterns reveal the distribution rules of spatial features, which can be valuable for application users. This book provides commercial software developers with proven and effective algorithms for detecting and filtering these implicit patterns, and includes easily implemented pseudocode for all the algorithms. Furthermore, it offers a basis for further research in this promising field.Preference-based co-location pattern mining refers to mining constrained or condensed co-location patterns instead of mining all prevalent co-location patterns. Based on the authors¿ recent research, 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.Presenting a systematic, mathematical study of preference-based spatial co-location pattern mining, this book can be used both as a textbook for those new to the topic and as a reference resource for experienced professionals.

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Title
Preference-based Spatial Co-location Pattern Mining
Author
Lizhen Wang, Lihua Zhou, Yuan Fang
Publisher
Springer Nature Singapore
Publication year
2023
Condition
Hervorragend
Binding
Soft cover
Language
English
ISBN 10
9811675686
ISBN 13
9789811675683
Series
Book 4 of 13: Big Data Management
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