The self-organizing map, the Geo-SOM, and relevant variants for geosciences

Bação, Fernando; Lobo, Victor; Paìnho, Marco

doi:10.1016/j.cageo.2004.06.013

Cited by 98 publications

(44 citation statements)

References 7 publications

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“…These include (1) speed and quality of clustering; (2) the number of clusters; (3) the updating procedure for the output neurons; and (4) the learning rate in the SOM model. Recent work in applying the SOM model and its suggested variants (Cuadros-Vargas and Romero, 2002;Guo et al, 2003;Skupin and Fabrikant, 2003;Guo et al, 2004;Yang et al, 2003;Bação et al, 2004Bação et al, , 2005Huang et al, 2005;Cuadros-Vargas and Romero, 2005;Oyana et al, 2005a, b;Guo et al, 2006) have significant implications with regards to how we extract relevant information or gain fundamental insights from very large-scale datasets. These studies along with this one could be valuable in advancing our understanding of the biomedical and epidemiological processes of diseases in relation to space and time.…”

Section: Discussionmentioning

confidence: 99%

Visualization of High-Dimensional Clinically Acquired Geographic Data Using the Self-Organizing Maps

Oyana¹

2009

J ENV INFORM

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ABSTRACT. The objective of this study is to visualize high-dimensional data vectors using popular data reduction algorithms. The study reports on the effectiveness and expressiveness of a set of data reduction algorithms in the visualization of geospatial data sets derived from clinical records of patients. The experiments show that when the SOM algorithm is combined with GIS methods together they are even more powerful tools for exploratory analysis than when each is applied separately. The visual approach provides a very useful exploration environment to support the formulation of new and better study hypotheses regarding the spatial distribution of a particular disease. While it was apparent that the spatial distribution and patterns of asthma were predominately located near the major roadways and the Peace Bridge Complex, obstructive sleep apnea is slightly more widespread even in the suburbs and surrounding neighborhoods. The spatial patterns discovered between the original features of adult and childhood asthma are consistent with the SOM-trained data, but a slight difference emerges for the SOM-trained obstructive sleep apnea data set. This study is successful at gaining significant novel insights into the spatial characteristics of patient data in relation to key environmental factors.

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Section: Discussionmentioning

confidence: 99%

Visualization of High-Dimensional Clinically Acquired Geographic Data Using the Self-Organizing Maps

Oyana¹

2009

J ENV INFORM

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“…As k (the geographic tolerance) increases, the unit locations will no longer be quasi-proportional to the locations of the training patterns, and the "equivalent filter" functions of the units will become more and more skewed, eventually ceasing to be useful as models. For a detailed explanation of the workings of the GeoSOM the reader is referred to [20].…”

Section: Som and Geosommentioning

confidence: 99%

GeoSOM Suite: A Tool for Spatial Clustering

Henriques

Bação

Lobo

2009

Computational Science and Its Applications – ICCSA 2009

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Abstract. The large amount of spatial data available today demands the use of data mining tools for its analysis. One of the most used data mining techniques is clustering. Several methods for spatial clustering exist, but many consider space as just another variable. We present in this paper a tool particularly suited for spatial clustering: the GeoSOM suite. This tool implements the GeoSOM algorithm, which is based on Self-Organizing Maps. This paper describes this tool, and shows that it is adequate for exploring spatial data.

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“…This procedure can be used to incorporate spatial information in the algorithm, although it has to be noted that samples located far from the center of the data set are ill represented in the SOM. In order to overcome this problem, Bação et al (2005a) proposed the GEOSOM.…”

Section: Geosom and Geo3dsommentioning

confidence: 99%

“…Based on the premise that samples located close together are more likely to be related to each other than samples with a large distance between them, the incorporation of geographical coordinates in the EDA-algorithm provides a way of accounting for the spatial correlation in the exploratory data analysis. Bação et al (2005a) discusses this topic in relation to the SOM-algorithm and compares the standard SOM-analysis in which the geographic coordinates are considered as any other variable to the GEOSOM, a modified version of the SOM, designed to explicitly incorporate spatial information. Application of the GEOSOM on two artificial data sets and a real-world demographic data set revealed the ability of the GEOSOM to increase the spatial resolution of the clustering.…”

Section: Introductionmentioning

confidence: 99%

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Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen's Self-Organizing Map

Peeters

Bação

Lobo

et al. 2007

Hydrol. Earth Syst. Sci.

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Abstract. The use of unsupervised artificial neural network techniques like the self-organizing map (SOM) algorithm has proven to be a useful tool in exploratory data analysis and clustering of multivariate data sets. In this study a variant of the SOM-algorithm is proposed, the GEO3DSOM, capable of explicitly incorporating three-dimensional spatial knowledge into the algorithm. The performance of the GEO3DSOM is compared to the performance of the standard SOM in analyzing an artificial data set and a hydrochemical data set. The hydrochemical data set consists of 131 groundwater samples collected in two detritic, phreatic, Cenozoic aquifers in Central Belgium. Both techniques succeed very well in providing more insight in the groundwater quality data set, visualizing the relationships between variables, highlighting the main differences between groups of samples and pointing out anomalous wells and well screens. The GEO3DSOM however has the advantage to provide an increased resolution while still maintaining a good generalization of the data set.

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The self-organizing map, the Geo-SOM, and relevant variants for geosciences

Cited by 98 publications

References 7 publications

Visualization of High-Dimensional Clinically Acquired Geographic Data Using the Self-Organizing Maps

Visualization of High-Dimensional Clinically Acquired Geographic Data Using the Self-Organizing Maps

GeoSOM Suite: A Tool for Spatial Clustering

Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen's Self-Organizing Map

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