Visual analysis of self-organizing maps

Stefanovič, Pavel; Kurasova, Olga

doi:10.15388/na.16.4.14091

Cited by 59 publications

(39 citation statements)

References 10 publications

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“…The self-organizing maps began to be explored by T. Kohonen as of 1982, however their use is becoming more frequent [13]. The SOM-type networks present unmasked clustering process and perform the training based on the patterns of their spatial organization only in the homology (similarity) of the data, that is, without prior knowledge of the class to which they belong [21].…”

Section: Discussionmentioning

confidence: 99%

“…Map (SOM) neural networks are a type of exploratory multivariate analysis tool that allows, through artificial computational intelligence, to design high-dimensional data in a smaller dimensional space, without loss of information [12]. This new organization prioritizes maintaining the structure, such as clusters and information relationships [13]. This reinforces its constant use in several…”

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confidence: 99%

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Self-organized map: the new aproach for study of genetic divergence in kale

Brito

Júnior

Azevedo

et al. 2020

Preprint

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The objective of this study was to study the genetic divergence between genotypes of kale, to propose a methodology for the use of neural networks of the SOM type and to test its efficiency through Anderson's discriminant analysis. We evaluated 33 families of half-siblings of kale and three commercial cultivars. The design was a randomized block with four replications with six plants per plot. A total of 14 plant-level quantitative traits were evaluated. Genetic values were predicted at family level via REML / BLUP. For the study of divergence, neural networks of the SOM type (Selforganizing Map) were adopted. We evaluated different network architectures, whose consistencies of the clusters were identified by the Anderson discriminant analysis and by the number of empty clusters. After selecting the best network configuration, a dissimilarity matrix was obtained, from which a dendrogram was constructed using the UPGMA method. The best network architecture was formed with five rows and one column, totaling five neurons and consequently five clusters. The greatest dissimilarity was established between clusters I and V. The crossing between the genotypes of cluster I and those belonging to clusters III and V are the most recommended, since they aim to recombine families with characteristics of interest to the improvement and high dissimilarity. Anderson's discriminant analysis showed that the genotype classification was 100% correct, indicating the efficiency of the methodology used. Map (SOM) neural networks are a type of exploratory multivariate analysis tool that allows, through artificial computational intelligence, to design high-dimensional data in a smaller dimensional space, without loss of information [12]. This new organization prioritizes maintaining the structure, such as clusters and information relationships [13]. This reinforces its constant use in several

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Self-organized map: the new aproach for study of genetic divergence in kale

Brito

Júnior

Azevedo

et al. 2020

Preprint

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“…2.1 Self -Organizing Maps (SOM) -Konohen network Self-Organizing map or SOM, initialized by Professor T. Konohen since the early of 1982s, is undeniably useful clustering tool. Since it was introduced, SOM has been applied widely in Application of self organizing map in construction, geology and petroleum industry Pham Son Tung, Truong Minh Huy, Pham Ba Tuan S various fields such as psychology, economy, medical care, engineering and a vast majority of other professionals [1,3].…”

Section: Methodsmentioning

confidence: 99%

“…At the beginning of SOM algorithm, weighted vectors in the Konohen network have random values associated with different properties ranging from 0 to 1. After each iteration, these random values will be adjusted to a random input vector chosen from the normalized input data [3]. The number of iterations is usually 500 times bigger than the quantity of network nodes [1].…”

Section: Methodsmentioning

confidence: 99%

Application of self organizing map in construction, geology and petroleum industry

2017

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In recent years, Artificial Intelligence (AI) has become an emerging subject and been recognized as the flagship of the Fourth Industrial Revolution. AI is subtly growing and becoming vital in our daily life. Particularly, Self-Organizing Map (SOM), one of the major branches of AI, is a useful tool for clustering data and has been applied successfully and widespread in various aspects of human life such as psychology, economic, medical and technical fields like mechanical, construction and geology. In this paper, the primary purpose of the authors is to introduce SOM algorithm and its practical applications in geology and construction. The results are classification of rock facies versus depth in geology and clustering two sets of construction prices indices and building material costs indice.

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“…Despite lower precision of this package in comparison to Matlab Toolbox, distribution is more smooth and heteroscedastic with all neurons selforganized relatively to each other that is reflected in larger number of similar technological patterns between technologies as on the figure down. Closer structural similarities are reflected in closer topologies, while color reflects strength of connection with red as strongest connection yellow-weaker connection and black shows no connection with neural map [25] (Fig. 6).…”

Section: Colliders Comparisonmentioning

confidence: 99%

Particle colliders market development and evolution models

Заболотский

2020

SN Appl. Sci.

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Main goal of this article-assessment of scientific projects and their economic impact. This assessment includes economic and technological feasibility on the example of elementary physics collider projects. Methodology includes structural integration in other technological fields by means of self-organizing neural maps and anthological retrospective analysis of radical discoveries with their recent impetus for economic development. Self-organizing neural map helps to expose hidden structural similarities of technology spreading and integration with other technologies. This is a new approach exposing the capacity for technology convergence into other industries. Capacity evaluations for integration and emergence of new technologies on the frontiers of elementary particles physics is provided. Explanation of integration problems in explained. Novelty of the study is based on integration of physical attributes into economics enabling new explanations of technology development models. Risk adjusted net present value evaluation is carried out. Comparison of similar projects around the world created in the article shows common frames and model of collider projects development. Risk adjusted model takes into account integration potential of technology under study. Conclusion combining obtained results in neural maps and historical retrospection is made.

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Visual analysis of self-organizing maps

Cited by 59 publications

References 10 publications

Self-organized map: the new aproach for study of genetic divergence in kale

Self-organized map: the new aproach for study of genetic divergence in kale

Application of self organizing map in construction, geology and petroleum industry

Particle colliders market development and evolution models

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