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Preference-based spatial co-location...

Wang, Lizhen.

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Preference-based spatial co-location pattern mining

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Preference-based spatial co-location pattern mining/ by Lizhen Wang, Yuan Fang, Lihua Zhou.
作者:	Wang, Lizhen.
其他作者:	Fang, Yuan.
出版者:	Singapore :Springer Singapore : : 2022.,
面頁冊數:	xvi, 294 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Spatial data mining. -
電子資源:	https://doi.org/10.1007/978-981-16-7566-9
ISBN:	9789811675669

Preference-based spatial co-location pattern mining
Wang, Lizhen.

Preference-based spatial co-location pattern mining[electronic resource] /by Lizhen Wang, Yuan Fang, Lihua Zhou. - Singapore :Springer Singapore :2022. - xvi, 294 p. :ill., digital ;24 cm. - Big data management,2522-0187. - 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.

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.

ISBN: 9789811675669

Standard No.: 10.1007/978-981-16-7566-9doiSubjects--Topical Terms:

3492384
Spatial data mining.

LC Class. No.: QA76.9.D343 / W36 2022

Dewey Class. No.: 006.312

Preference-based spatial co-location pattern mining
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520 $a 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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