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Preference-based Spatial Co-location Pattern Mining

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...

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Bibliographic Details
Main Authors: Wang, Lizhen, Fang, Yuan (Author), Zhou, Lihua (Author)
Format: eBook
Language:English
Published: Singapore Springer Nature Singapore 2022, 2022
Edition:1st ed. 2022
Series:Big Data Management
Subjects:
Computer Science
Blockchains (databases)
Data Protection
Blockchain
Data And Information Security
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • 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

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