Name
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICDMW.2007.101 | ICDM Workshops |
Keywords | Field | DocType |
multivariate normal distribution,likelihood function,specific spatial covariance structure,variance-covariance matrix,spatially close,similar multivariate observation,spatial clustering,cluster similar observation,spatial location,spatially close observation,similar characteristic,group multivariate observation,geographic location,covariance matrix,variance covariance matrix,categorical data,spatial pattern,covariance function,geostatistics | Covariance function,Likelihood function,Pattern recognition,Multivariate statistics,Matrix (mathematics),Multivariate normal distribution,Artificial intelligence,Covariance matrix,Cluster analysis,Statistics,Geostatistics,Mathematics | Conference |
ISBN | Citations | PageRank |
0-7695-3033-8 | 0 | 0.34 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| April Kerby | 1 | 0 | 0.34 |
| David Marx | 2 | 79 | 5.70 |
| A Samal | 3 | 1033 | 213.54 |
| Viacheslav Adamchuck | 4 | 0 | 0.34 |