Optimal decisions in combining the SOM with nonlinear projection methods

Bernatavičienė, Jolita; Dzemyda, Gintautas; Kurasova, Olga; Marcinkevičius, Virginijus

doi:10.1016/j.ejor.2005.05.030

Cited by 19 publications

(12 citation statements)

References 19 publications

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“…This is difficult to visualize and small differences between system states are disregarded. Some authors combine SOMs with nonlinear projection methods to capture the temporal aspect of system state development (Bernataviciene et al 2006;Mustonen et al 2008;Lischeid 2009). To emphasize the temporal resolution and visualization of variation in the original data set, especially over time, the output of the SOMs were further subjected to the Sammon's mapping algorithm (Sammon 1969).…”

Section: Discussionmentioning

confidence: 99%

Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping

Page

Huggenberger

Lischeid

2015

Water Resour Manage

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Groundwater extracted from alluvial aquifers close to rivers is vulnerable to contamination by infiltrating river water. Infiltration is often increased during high discharge events, when the levels of waterborne pathogens are also increased. Water suppliers with low-level treatment thus rely on alternative measures derived from information on system state to manage the resource and maintain drinking-water quality. In this study, a combination of Self-Organizing Maps and Sammon's Mapping (SOM-SM) was used as a proxy analysis of a multivariate time-series to detect critical system states whereby contamination of the drinking water extraction wells is imminent. Groundwater head, temperature and electrical conductivity time-series from groundwater observation wells were analysed using the SOM-SM method. Independent measurements (spectral absorption coefficient, turbidity, particle density and river stage) were used. This approach can identify critical system states and can be integrated into an adaptive, online, automated groundwater-management process.

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Section: Discussionmentioning

confidence: 99%

Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping

Page

Huggenberger

Lischeid

2015

Water Resour Manage

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“…SOMs can be applied for clustering, reduction of the dimensionality of the data, and its visualization. Although there have been many modifications of SOMs [47,48], the general Kohonen algorithm [43] realized in the SOM toolbox [46] was used here for the NANSEN decision-support system.…”

Section: Model-base and Its Management Subsystemmentioning

confidence: 99%

Artificial Neural Network-Based Decision Support System for Development of an Energy-Efficient Built Environment

et al. 2018

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Implementing energy-efficient solutions in a built environment is important for reaching international energy reduction targets. For advanced energy efficiency-related solutions, computer-based decision support systems are proposed and rapidly used in a variety of spheres relevant to a built environment. Present research proposes a novel artificial neural network-based decision support system for development of an energy-efficient built environment. The system was developed by integrating methods of the multiple criteria evaluation and multivariant design, determination of project utility and market value, and visual data mining by artificial neural networks. It enables a user to compose up to 100,000,000 combinations of the energy-efficient solutions, analyze strengths and weaknesses of a built environment projects, provide advice for stakeholders, and calculate market value and utility degree of the projects. For visual data mining, self-organizing maps (type neural networks) are used, which may influence the choosing of the final set of alternatives and criteria in the decision-making problem, taking into account the discovered similarities of alternatives or criteria. A system was validated by the real case study on the design of an energy-efficient individual house.

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“…The visualization allows us to comprehend data and processes [1,2,3,4,5,6]. We have proposed to combine these two groups of methods in order to reduce the number of items and dimensionality [2,7,8,9,10,11].…”

Section: Reducing the Number Of Data Items And Their Dimensionalitymentioning

confidence: 99%

“…A smaller dataset can be used by MDS and the time is saved. The consecutive combination of vector quantization methods and the multidimensional scaling ( Figure 1) have been investigated in [7,8,9,10,11,19]. So, the reason to use the combination is a desire to decrease the computation time without losing the quality of mapping (visualization).…”

Section: Integration Of Vector Quantization and Visualizationmentioning

confidence: 99%

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Integration of the Self-Organizing Map and Neural Gas With Multidimensional Scaling

Kurasova¹,

Molytė²

2011

ITC

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In the paper, two combinations (consecutive and integrated) of vector quantization methods (self-organizing map and neural gas) and multidimensional scaling (MDS) have been investigated and compared. The vector quantization is used to reduce the number of dataset items. The dataset with a smaller number of items is analyzed by multidimensional scaling in order to reduce the number of features of data (dimensionality of space) and to map them onto the plane, i.e., to visualize. Some ways of the initialization (at random, on a line, by PCs and by variances) of twodimensional vectors in MDS have been investigated. Two ways of assignment of two-dimensional vectors in the integrated combinations of MDS and vector quantization methods have been examined, too.

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Optimal decisions in combining the SOM with nonlinear projection methods

Cited by 19 publications

References 19 publications

Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping

Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping

Artificial Neural Network-Based Decision Support System for Development of an Energy-Efficient Built Environment

Integration of the Self-Organizing Map and Neural Gas With Multidimensional Scaling

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