Spatial clustering using the likelihood function

April Kerby, David Marx, Ashok Samal, Viacheslav Adamchuck

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Clustering has been widely used as a tool to group multivariate observations that have similar characteristics. However, there have been few attempts at formulating a method to group similar multivariate observations while taking into account their spatial location [12, 13, 14]. This paper proposes a method to spatially cluster similar observations based on their likelihoods. The geographic or spatial location of the observations can be incorporated into the likelihood of the multivariate normal distribution through the variance-covariance matrix. The variance-covariance matrix can be computed using any specific spatial covariance structure. Therefore, observations within a cluster which are spatially close to one another will have a larger likelihood than those observations which are not close to one another. This results in spatially close observations being placed into the same cluster.

Original languageEnglish (US)
Title of host publicationICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
Pages637-642
Number of pages6
DOIs
StatePublished - 2007
Event17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE, United States
Duration: Oct 28 2007Oct 31 2007

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007
CountryUnited States
CityOmaha, NE
Period10/28/0710/31/07

ASJC Scopus subject areas

  • Engineering(all)

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