Mining Co-Location Patterns with Rare Events from Spatial Data Sets View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2006-09

AUTHORS

Yan Huang, Jian Pei, Hui Xiong

ABSTRACT

A co-location pattern is a group of spatial features/events that are frequently co-located in the same region. For example, human cases of West Nile Virus often occur in regions with poor mosquito control and the presence of birds. For co-location pattern mining, previous studies often emphasize the equal participation of every spatial feature. As a result, interesting patterns involving events with substantially different frequency cannot be captured. In this paper, we address the problem of mining co-location patterns with rare spatial features. Specifically, we first propose a new measure called the maximal participation ratio (maxPR) and show that a co-location pattern with a relatively high maxPR value corresponds to a co-location pattern containing rare spatial events. Furthermore, we identify a weak monotonicity property of the maxPR measure. This property can help to develop an efficient algorithm to mine patterns with high maxPR values. As demonstrated by our experiments, our approach is effective in identifying co-location patterns with rare events, and is efficient and scalable for large-scale data sets. More... »

PAGES

239-260

References to SciGraph publications

  • 1995. Discovery of spatial association rules in geographic information databases in ADVANCES IN SPATIAL DATABASES
  • 1997. Finding boundary shape matching relationships in spatial data in ADVANCES IN SPATIAL DATABASES
  • 1998. Discovering associations in spatial data — An efficient medoid based approach in RESEARCH AND DEVELOPMENT IN KNOWLEDGE DISCOVERY AND DATA MINING
  • 2001. Discovering Spatial Co-location Patterns: A Summary of Results in ADVANCES IN SPATIAL AND TEMPORAL DATABASES
  • 1994-10. An introduction to spatial database systems in THE VLDB JOURNAL
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10707-006-9827-8

    DOI

    http://dx.doi.org/10.1007/s10707-006-9827-8

    DIMENSIONS

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