Title

Mining Spatial Colocation Patterns: A Different Framework

Document Type

Article

Publication Date

1-2012

Publication Source

Data Mining and Knowledge Discovery Journal

Volume

24

Issue

1

Inclusive pages

159-194

DOI

http://dx.doi.org/10.1007/s10618-011-0223-0

Publisher

Springer Science & Business Media

Place of Publication

New York

ISBN/ISSN

13845810

Abstract

Recently, there has been considerable interest inmining spatial colocation patterns from large spatial datasets. Spatial colocation patterns represent the subsets of spatial events whose instances are often located in close geographic proximity. Most studies of spatial colocation mining require the specification of two parameter constraints to find interesting colocation patterns. One is a minimum prevalent threshold of colocations, and the other is a distance threshold to define spatial neighborhood. However, it is difficult for users to decide appropriate threshold values without prior knowledge of their task-specific spatial data. In this paper, we propose a different framework for spatial colocation pattern mining. To remove the first constraint, we propose the problem of finding N-most prevalent colocated event sets, where N is the desired number of colocated event sets with the highest interest measure values per each pattern size.We developed two alternative algorithms for mining the N-most patterns. They reduce candidate events effectively and use a filter-and-refine strategy for efficiently finding colocation instances from a spatial dataset. We prove the algorithms are correct and complete in finding the N-most prevalent colocation patterns. For the second constraint, a distance threshold for spatial neighborhood determination, we present various methods to estimate appropriate distance bounds from user input data. The result can help an user to set a distance for a conceptualization of spatial neighborhood. Our experimental results with real and synthetic datasets show that our algorithmic design is computationally effective in finding the N-most prevalent colocation patterns. The discovered patterns were different depending on the distance threshold, which shows that it is important to select appropriate neighbor distances.

Keywords

Spatial data mining, Spatial association patterns, Colocation mining

Disciplines

Computer Sciences

This document is currently not available here.

  Contact Author

Share

COinS