The application of fuzzy c-means cluster analysis and non-linear mapping to geochemical datasets: examples from Portugal

Vriend, S.P.; Gaans, P.F.M. van; Middelburg, Jack J.; Nijs, A. de

doi:10.1016/0883-2927(88)90009-1

Cited by 83 publications

(44 citation statements)

References 12 publications

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“…Finally, having obtained multi-parameter magnetic data sets, two multivariate statistical methods can be applied to robustly characterize and/or differentiate the sediments; cluster analysis (using fuzzy c-means) and non-linear mapping, using one of the available programs (e.g. Vriend et al, 1988). Both techniques have been applied successfully to a number of environmental data sets (Maher et al, 2008).…”

Section: XLIIImentioning

confidence: 99%

The Application of Environmental Magnetism Techniques for Pollution Assessment in Urban and Suburban Areas in Greece: State of the Art and Case Studies

2017

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Section: XLIIImentioning

confidence: 99%

The Application of Environmental Magnetism Techniques for Pollution Assessment in Urban and Suburban Areas in Greece: State of the Art and Case Studies

2017

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“…We are not the first who use k-means or its soft version fuzzy k-means algorithms to classify geospatial phenomenon. It has been successfully used in geohydrology, soil science and vegetation mapping (Vriend et al 1988, de Bruin, Stein 1998, Burrough. McDonnell 1998, Burrough et al 2000, 2001, Schmidt, Hewitt 2004.…”

Section: K-means Unsupervised Classification Algorithmmentioning

confidence: 99%

Semi-Automated Classification of Landform Elements in Armenia Based on SRTM DEM using K-Means Unsupervised Classification

Piloyan

Konečný

2017

Quaestiones Geographicae

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PiloyAn A., Konečný M., 2017. Semi-automated classification of landform elements in Armenia based on SRTM DEM using k-means unsupervised classification. Quaestiones Geographicae 36(1), Bogucki Wydawnictwo Naukowe, Poznań, pp. 93-103, 5 figs, 4 tables.AbstrAct: Land elements have been used as basic landform descriptors in many science disciplines, including soil mapping, vegetation mapping, and landscape ecology. This paper presents a semi-automatic method based on k-means unsupervised classification to analyze geomorphometric features as landform elements in Armenia. First, several data layers were derived from DEM: elevation, slope, profile curvature, plan curvature and flow path length. Then, k-means algorithm has been used for classifying landform elements based on these morphomertic parameters. The classification has seven landform classes. Overall, landform classification is performed in the form of a three-level hierarchical scheme. The resulting map reflects the general topography and landform character of Armenia.

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“…Boundaries among objects of different categories are sometimes ambiguous, thus causing misclassification of geochemically similar members into entirely different clusters. Various degrees of overlap are encountered because naturally occurring materials are often not well separated in homogeneous groupings, for instance, in mixed samples (Yu & Xie, 1985;Vriend et al, 1988). Fuzzy c-means (FCM) clustering may give a better performance.…”

Section: Fuzzy C-means Clustering and Non-linear Mappingmentioning

confidence: 99%

“…Fuzzy c-means (FCM) clustering may give a better performance. Vriend et al (1988) showed that a combination of FCM and Table 1. Basic statistics for the Aruba stream-sediment data set.…”

Section: Fuzzy C-means Clustering and Non-linear Mappingmentioning

confidence: 99%

Aruba - a geochemical baseline study

Oever¹

2000

Netherlands Journal of Geosciences

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A stream-sediment survey was carried out for the island of Aruba. Concentrations in stream sediments represent the abundance of chemical elements in the drainage basins. A geochemical atlas was created from the collected data and natural background values were established. Cluster analysis and pattern recognition techniques were used to gain a better understanding of the data set.Two cluster models were selected to study the various geochemical controls on the sediments and to establish a spatial basis of environmental-quality settings for the development of future environmental policies. The first cluster model was suitable to recognise in some detail the reflection of the geology on the geochemistry of the stream sediments. The second, coarser cluster model stressed the importance to distinguish between the two main Iithological units of the island when instituting natural background values. Not one uniform value per element is valid, but the value depends on the lithology.

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The application of fuzzy c-means cluster analysis and non-linear mapping to geochemical datasets: examples from Portugal

Cited by 83 publications

References 12 publications

The Application of Environmental Magnetism Techniques for Pollution Assessment in Urban and Suburban Areas in Greece: State of the Art and Case Studies

The Application of Environmental Magnetism Techniques for Pollution Assessment in Urban and Suburban Areas in Greece: State of the Art and Case Studies

Semi-Automated Classification of Landform Elements in Armenia Based on SRTM DEM using K-Means Unsupervised Classification

Aruba - a geochemical baseline study

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