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Title
Preference-based spatial co-location pattern mining / Lizhen Wang, Yuan Fang, Lihua Zhou.
ISBN
9789811675669 (electronic bk.)
981167566X (electronic bk.)
9789811675652
9811675651
Published
Singapore : Springer ; Beijing : Science Press, [2022]
Copyright
©2022
Language
English
Description
1 online resource (307 pages).
Item Number
10.1007/978-981-16-7566-9 doi
Call Number
QA76.9.D343 W36 2022
Dewey Decimal Classification
006.3/12
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Description based upon print version of record.
Series
Big data management.
Chapter 1: Introduction
Chapter 2: Maximal Prevalent Co-location Patterns
Chapter 3: Maximal Sub-prevalent Co-location Patterns
Chapter 4: SPI-Closed Prevalent Co-location Patterns
Chapter 5: Top-k Probabilistically Prevalent Co-location Patterns
Chapter 6: Non-Redundant Prevalent Co-location Patterns
Chapter 7: Dominant Spatial Co-location Patterns
Chapter 8: High Utility Co-location Patterns
Chapter 9: High Utility Co-location Patterns with Instance Utility
Chapter 10: Interactively Post-mining User-preferred Co-location Pat-terns with a Probabilistic Model
Chapter 11: Vector-Degree: A General Similarity Measure for Spatial Co-Location Patterns.
