Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.csda.2004.12.005
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
Source: 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. https://doi.org/10.1016/j.csda.2004.12.005
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
URI: http://scholarbank.nus.edu.sg/handle/10635/39772
ISSN: 01679473
DOI: 10.1016/j.csda.2004.12.005
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

14
checked on Dec 11, 2017

WEB OF SCIENCETM
Citations

12
checked on Dec 11, 2017

Page view(s)

44
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.