A SOM based cluster visualization and its application for false coloring

Himberg, Johan

doi:10.1109/ijcnn.2000.861379

Cited by 47 publications

(41 citation statements)

References 3 publications

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“…The spatial distribution of the groups is organized per well screen, with screen 1 being the shallowest screen and screen 3 the deepest. The false coloring of the units of the self-organizing map is carried out in such a way that units with similar weight vectors have similar colors, using a naive contraction model based on the U-matrix according to the algorithm proposed by Himberg (2000). The resulting color coding is then applied to the representation of the well screens.…”

Section: Hydrochemical Data Setmentioning

confidence: 99%

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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Section: Hydrochemical Data Setmentioning

confidence: 99%

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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“…Hills or walls represent larger distances indicating dissimilarities of the input data. Alternative visualization tools for SOMs have also been proposed, such as the agglomerative clustering where the SOM neighborhood relation can be used to construct a dendrogram on the map (Vesanto, Alhoniemi 2000), and a hierarchical clustering of the units on the map with a simple contraction model (Himberg 2000).…”

Section: The Basic Som-algorithmmentioning

confidence: 99%

“…Often these additional visualization techniques lead to an enhanced interpretation of the trajectory. Other interesting visualization techniques for SOMs consider hierarchical agglomerations of the map where the SOM neighborhood relation can be used to construct a dendrogram on the map (Murtagh 1995;Vesanto, Alhoniemi 2000;Himberg 2000).…”

Section: Temporal Sequence Processing Without Modifying the Basic Sommentioning

confidence: 99%

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Classification and Clustering in Business Cycle Analysis.

Heilemann¹,

Weihs²

2007

Schriften Des Rheinisch-Westfälischen Instituts Für Wirtschaftsforschung

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PrefaceThe analysis of cyclical macroeconomic phenomena is an important field of econometric research. In the recent past, research interests have de-emphasized quantitative forecasting exercises and have addressed the qualitative diagnosis of the relative stance of the economy regarding "upswing", "recession", or "boom" periods, i. e. the classification of the state of the economy into a limited number of discrete states. In this context the principal challenge is to reduce the multifaceted and sometimes abundant quantitative information about the business cycle to such qualitative statements in an efficient way. For more than six years this task was the focus of the project "Multivariate determination and analysis of business cycles" within the SFB 475 "Reduction of complexity in multivariate data structures", funded by the German Research Foundation (DFG).The necessity for complexity reduction is, of course, not unique to business cycle analysis but is studied in many fields and in a number of ways. This broad interest in the reduction of problem dimensionality and in the appropriate combination of data and of theory caused the RWI Essen and the Statistical Department of the University of Dortmund in January 2002 to hold a workshop at the RWI Essen where the findings of this and similar projects were presented and discussed. The present publication collects revised versions of the papers presented at this workshop. Although the workshop took place some five years ago, these papers mark an importment juncture in the development of business cycle research.The RWI Essen thanks the editors of the volume and the authors for their contributions and the other participants for lively discussions. Particular thanks have to go to Joachim Schmidt who supervised the production of the volume and to Anette Hermanowski and Claudia Lohkamp whose support of the production of the manuscripts and of the organisation of the workshop was indispensable.Essen,

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“…Another way to obtain a 2D projection is by contracting the topological coordinates of the pre-specified 2D grid using the distances among the prototype vectors. New grid coordinates can be estimated as the weighted average of the prototype vector locations (Himberg, 2000). The weights are defined as the similarities between prototype vector pairs, s i j .…”

Section: Som Plotted Against 2d Colormapsmentioning

confidence: 99%

Seismic interpretation of self-organizing maps using 2D color displays

2010

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ABSTRACT. Classification without supervision of patterns into groups is formally called clustering. Depending on the application area these patterns are called data lists, observations or vectors. For exploration geophysicists, these patterns are usually associated with seismic attributes, seismic waveforms or seismic facies. The main objective of this paper is to show how one of the most popular clustering algorithms -Kohonen self-organizing maps, can be applied to enhance seismic interpretation analysis associated with one and two-dimensional colormaps.

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A SOM based cluster visualization and its application for false coloring

Cited by 47 publications

References 3 publications

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

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

Classification and Clustering in Business Cycle Analysis.

Seismic interpretation of self-organizing maps using 2D color displays

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