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Title: A hybrid EM approach to spatial clustering
Authors: Hu, T.
Sung, S.Y. 
Keywords: Expectation-maximization algorithm
Gaussian mixture
Spatial clustering
Spatial penalty term
Issue Date: 2006
Citation: Hu, T., Sung, S.Y. (2006). A hybrid EM approach to spatial clustering. Computational Statistics and Data Analysis 50 (5) : 1188-1205. ScholarBank@NUS Repository.
Abstract: Spatial clustering requires consideration of spatial information and this makes expectation-maximization (EM) algorithm that maximizes likelihood alone inappropriate. Although neighborhood EM (NEM) algorithm incorporates a spatial penalty term, it needs much more iterations for E-step. To incorporate spatial information while avoiding much additional computation, we propose a hybrid EM (HEM) approach that combines EM and NEM. Early training is performed via a selective hard EM till the penalized likelihood criterion begins to decrease. Then training is turned to NEM, which runs only one iteration of E-step and plays a role of finer tuning. Thus spatial information is incorporated throughout HEM and the computational complexity is also comparable to EM. Empirical results show that a few more passes are needed in HEM to converge after switching to NEM and the final clustering quality is close to or slightly better than standard NEM. © 2005 Elsevier B.V. All rights reserved.
Source Title: Computational Statistics and Data Analysis
ISSN: 01679473
DOI: 10.1016/j.csda.2004.12.005
Appears in Collections:Staff Publications

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