Showing papers on "Ward's method published in 2006"
TL;DR: The self-organizing map (SOM) is applied to identify the homogeneous regions for regional frequency analysis and shows that the SOM can identify thehomogeneous regions more accurately as compared to the other two clustering methods.
176 citations
TL;DR: The conclusion is that model‐based methods are certainly worthwhile for continuous data, however, their benefit, in particular their ability to deal with correlated data, is not marked for ordinal data.
Abstract: Cluster analysis can be used to identify homogenous subgroups in many fields, including psychology and psychiatry. However, most clustering methods implemented in general-purpose statistical packages are heuristic and can be criticized in principle for their lack of an underlying statistical model. Furthermore correlations between variables are generally ignored by standard methods. The question addressed here is whether currently available commercial software (S-PLUS), which provides model-based methods for clustering correlated continuous data, should be used for clustering data derived from questionnaires. Such data may be either continuous or ordinal in nature and typically exhibit correlations. Performance is assessed in this study on simulated data sets containing distinct multivariate normal subpopulations, both before and after mapping the simulated data onto an ordinal scale. A practical example showing how correlated data can be cluster-analysed using these methods is given. The conclusion is that model-based methods are certainly worthwhile for continuous data. However, their benefit, in particular their ability to deal with correlated data, is not marked for ordinal data. Simpler methods such as Ward's method may be almost as effective in this situation. Copyright © 2006 John Wiley & Sons, Ltd.
4 citations
01 Sep 2006
TL;DR: A contextual unsupervised classification method of geostatistical data based on combination of Ward clustering method and Markov random fields (MRF) is proposed and a new measure of spatial adjacency of the classes is introduced.
Abstract: We propose a contextual unsupervised classification method of geostatistical data based on combination of Ward clustering method and Markov random fields (MRF). Image is clustered into classes by using not only spectrum of pixels but also spatial information. For the classification of remote sensing data of low spatial resolution, the treatment of mixed pixel is importance. From the knowledge that the most of mixed pixels locate in boundaries of land-covers, we first detect edge pixels and remove them from the image. We here introduce a new measure of spatial adjacency of the classes. Spatial adjacency is used to MRF-based update of the classes. Clustering of edge pixels are processed as final step. It is shown that the proposed method gives higher accuracy than conventional clustering method does.