Classification into homogeneous groups using combined cluster and discriminant analysis

Kovács, József; Kovács, Solt; Magyar, Norbert; Tanos, Péter; Hatvani, István Gábor; Anda, Angéla

doi:10.1016/j.envsoft.2014.01.010

Cited by 42 publications

(27 citation statements)

References 17 publications

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“…For a given grouping, one can take the discriminant functions as a decision rule to classify the original observations. Comparing the predicted class labels with the true class label eventually leads to a percentage value describing the ratio of correctly classified observations (the so called "ratio" value) by the linear plane [3]. The latter is a measure for the separability of the groups within that specific grouping, which tells something about the similarity of groups.…”

Section: Ccda Methodsmentioning

confidence: 99%

“…In these cases, the groups could at most be regarded as similar, the degree of similarity being described by the difference value; the closer this is to zero, the more similar the groups. The details about CCDA can be found in [3] and the corresponding "ccda" R package in [28].…”

Section: Ccda Methodsmentioning

confidence: 99%

“…The result is a spatial pattern which shows the relationship of the sampling sites (homogeneity or difference and in particular the rate of the latter). This method has been used successfully for hydrological and hydrogeological interpretation [3][4][5] and biological analysis [6]. In this paper, CCDA is used in a sedimentological problem.…”

Section: Introductionmentioning

confidence: 99%

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Interpretation of sedimentological processes of coarse-grained deposits applying a novel combined cluster and discriminant analysis

Farics

Kovács

et al. 2017

Open Geosciences

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Abstract:The main aim of this paper is to determine the depositional environments of an Upper-Eocene coarsegrained clastic succession in the Buda Hills, Hungary. First of all, we measured some commonly used parameters of samples (size, amount, roundness and sphericity) in a much more objective overall and faster way than with traditional measurement approaches, using the newly developed Rock Analyst application. For the multivariate data obtained, we applied Combined Cluster and Discriminant Analysis (CCDA) in order to determine homogeneous groups of the sampling locations based on the quantitative composition of the conglomerate as well as the shape parameters (roundness and sphericity). The result is the spatial pattern of these groups, which assists with the interpretation of the depositional processes. According to our concept, those sampling sites which belong to the same homogeneous groups were likely formed under similar geological circumstances and by similar geological processes. In the Buda Hills, we were able to distinguish various sedimentological environments within the area based on the results: fan, intermittent stream or marine.

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Section: Ccda Methodsmentioning

confidence: 99%

Section: Ccda Methodsmentioning

confidence: 99%

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Interpretation of sedimentological processes of coarse-grained deposits applying a novel combined cluster and discriminant analysis

Farics

Kovács

et al. 2017

Open Geosciences

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“…the difference d = ratio-q_95 is positive), then at the level of alpha = 0.05 the given classification is not homogeneous. Suggestions for the necessary subdivision of groups (step III), a more detailed description of the method in general, as well as details about the R package "CCDA" used for the computations in this study can be found in Kovács et al (2014).…”

Section: Appendix Amentioning

confidence: 99%

“…Combined cluster and discriminant analysis (CCDA) was used, first introduced by Kovács et al (2014) to find not only similar, but homogeneous groups. During the search process a decision has to be made whether further division of some groups is necessary, or not.…”

Section: Appendix Amentioning

confidence: 99%

Developments in water quality monitoring and management in large river catchments using the Danube River as an example

Chapman

Bradley

Gettel

et al. 2016

Environmental Science & Policy

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102

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Effective management of water quality in large rivers requires information on the influence of activities within the catchment (urban and rural) throughout the whole river basin. However, traditional water quality monitoring programmes undertaken by individual agencies normally relate to specific objectives, such as meeting quality criteria for wastewater discharges, and fail to provide information on basin-scale impacts, especially in transboundary river basins. Ideally, monitoring in large international river basins should be harmonised to provide a basin-scale assessment of sources and impacts of human activities, and the effectiveness of management actions. This paper examines current water quality issues in the Danube River Basin and evaluates the approach to water quality monitoring in the context of providing information for a basin-wide management plan. Lessons learned from the monitoring programme in the Danube are used to suggest alternative approaches that could result in more efficient generation of water quality data and provide new insights into causes and impacts of variations in water quality in other large international river basins.

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An evaluation of potential sampling locations in a reservoir with emphasis on conserved spatial correlation structure

Yenilmez

Düzgün

Aksoy

2014

Environ Monit Assess

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In this study, kernel density estimation (KDE) was coupled with ordinary two-dimensional kriging (OK) to reduce the number of sampling locations in measurement and kriging of dissolved oxygen (DO) concentrations in Porsuk Dam Reservoir (PDR). Conservation of the spatial correlation structure in the DO distribution was a target. KDE was used as a tool to aid in identification of the sampling locations that would be removed from the sampling network in order to decrease the total number of samples. Accordingly, several networks were generated in which sampling locations were reduced from 65 to 10 in increments of 4 or 5 points at a time based on kernel density maps. DO variograms were constructed, and DO values in PDR were kriged. Performance of the networks in DO estimations were evaluated through various error metrics, standard error maps (SEM), and whether the spatial correlation structure was conserved or not. Results indicated that smaller number of sampling points resulted in loss of information in regard to spatial correlation structure in DO. The minimum representative sampling points for PDR was 35. Efficacy of the sampling location selection method was tested against the networks generated by experts. It was shown that the evaluation approach proposed in this study provided a better sampling network design in which the spatial correlation structure of DO was sustained for kriging.

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Classification into homogeneous groups using combined cluster and discriminant analysis

Cited by 42 publications

References 17 publications

Interpretation of sedimentological processes of coarse-grained deposits applying a novel combined cluster and discriminant analysis

Interpretation of sedimentological processes of coarse-grained deposits applying a novel combined cluster and discriminant analysis

Developments in water quality monitoring and management in large river catchments using the Danube River as an example

An evaluation of potential sampling locations in a reservoir with emphasis on conserved spatial correlation structure

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