Spatial clustering using the likelihood function

April Kerby; David Marx; Ashok Samal; Viacheslav Adamchuck

doi:10.1109/ICDMW.2007.85

Spatial clustering using the likelihood function

April Kerby, David Marx, Ashok Samal, Viacheslav Adamchuck

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 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 language	English (US)
Title of host publication	ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
Pages	637-642
Number of pages	6
DOIs	https://doi.org/10.1109/ICDMW.2007.85
State	Published - 2007
Event	17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE, United States Duration: Oct 28 2007 → Oct 31 2007

Publication series

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

Conference

Conference	17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007
Country/Territory	United States
City	Omaha, NE
Period	10/28/07 → 10/31/07

ASJC Scopus subject areas

General Engineering

Access to Document

10.1109/ICDMW.2007.85

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Spatial clustering using the likelihood function. / Kerby, April; Marx, David; Samal, Ashok et al.
ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops. 2007. p. 637-642 4476735 (Proceedings - IEEE International Conference on Data Mining, ICDM).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kerby, A, Marx, D, Samal, A & Adamchuck, V 2007, Spatial clustering using the likelihood function. in ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops., 4476735, Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 637-642, 17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007, Omaha, NE, United States, 10/28/07. https://doi.org/10.1109/ICDMW.2007.85

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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.",

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Spatial clustering using the likelihood function

Abstract

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