Self-organizing maps and clustering methods for matrix data

2004 ◽  
Vol 17 (8-9) ◽  
pp. 1211-1229 ◽  
Author(s):  
Sambu Seo ◽  
Klaus Obermayer
Keyword(s):  
Clustering Methods ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Microarray Images

2018 ◽  
Vol 7 (1) ◽  
pp. 78
Author(s):  
Durga Prasad Kondisetty ◽  
Mohammed Ali Hussain
Keyword(s):  
Computational Cost ◽  
Microarray Technology ◽  
Qualitative And Quantitative Analysis ◽  
Clustering Methods ◽  
Self Organizing Maps ◽  
Microarray Image ◽  
Segmentation Quality ◽  
Som Algorithm ◽  
Simple Linear Iterative Clustering ◽  
Self Organizing

We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy c-means and self organizing maps clustering methods.

scholarly journals ELECTROFACIES CLASSIFICATION OF PONTA GROSSA FORMATION BY MULTI-RESOLUTION GRAPH-BASED CLUSTERING (MRGC) AND SELF-ORGANIZING MAPS (SOM) METHODS

2020 ◽  
Vol 38 (1) ◽  
pp. 52
Author(s):  
Felipe Vasconcelos dos Passos ◽  
Marco Antonio Braga ◽  
Thiago Gonçalves Carelli ◽  
Josiane Branco Plantz
Keyword(s):  
Gamma Ray ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Self Organizing Maps ◽  
Resolution Graph ◽  
Lithology Prediction ◽  
Paraná Basin ◽  
Parana Basin ◽  
Graph Based Clustering ◽  
Self Organizing

ABSTRACT. In Ponta Grossa Formation, devonian interval of Paraná Basin, Brazil, sampling restrictions are frequent, and lithological interpretations from gamma ray logs are common. However, no single log can discriminate lithology unambiguously. An alternative to reduce the uncertainty of these assessments is to perform multivariate analysis of well logs using data clustering methods. In this sense, this study aims to apply two different clustering algorithms, trained with gamma ray, sonic and resistivity logs. Five electrofacies were differentiated and validated by core data. It was found that one of the electrofacies identified by the model was not distinguished by macroscopic descriptions. However, the model developed is sufficiently accurate for lithological predictions.Keywords: geophysical well logging, lithology prediction, Paraná Basin. CLASSIFICAÇÃO DE ELETROFÁCIES DA FORMAÇÃO PONTA GROSSA UTILIZANDO OS MÉTODOS MULTI-RESOLUTION GRAPH-BASED CLUSTERING (MRGC) E SELF-ORGANIZING MAPS (SOM)RESUMO. Na Formação Ponta Grossa, intervalo devoniano da Bacia do Paraná, Brasil, restrições de amostragem são frequentes e interpretações litológicas dos registros de raios gama são comuns. No entanto, nenhum perfil geofísico único pode discriminar litologias sem ambiguidade. Uma alternativa para reduzir a incerteza dessas avaliações é executar uma análise multivariada combinando vários perfis geofísicos de poços por meio de métodos de agrupamento de dados. Nesse sentido, este estudo tem como objetivo aplicar dois algoritmos de agrupamento aos registros de raios gama, sônico e resistividade para fins de predição litológica. Cinco eletrofácies foram diferenciadas e validadas por dados de testemunhos. Verificou-se que uma classe identificada pelo modelo não foi identificada por descrições macroscópicas. Porém, o modelo é suficientemente preciso para predições litológicas.Palavras-chave: geofísica de poços, predição litológica, correlação rocha-perfil, Bacia do Paraná.

scholarly journals Business Client Segmentation in Banking Using Self-Organizing Maps

2014 ◽  
Vol 8 (2) ◽  
pp. 32-41 ◽  
Author(s):  
Mirjana Pejić Bach ◽  
Sandro Juković ◽  
Ksenija Dumičić ◽  
Nataša Šarlija
Keyword(s):  
Foreign Trade ◽  
Selection Criteria ◽  
Clustering Algorithm ◽  
Annual Income ◽  
Clustering Methods ◽  
Self Organizing Maps ◽  
Marketing Activities ◽  
Ward Clustering ◽  
Statistical Clustering ◽  
Self Organizing

Abstract Segmentation in banking for the business client market is traditionally based on size measured in terms of income and the number of employees, and on statistical clustering methods (e.g. hierarchical clustering, k-means). The goal of the paper is to demonstrate that self-organizing maps (SOM) effectively extend the pool of possible criteria for segmentation of the business client market with more relevant criteria, including behavioral, demographic, personal, operational, situational, and cross-selling products. In order to attain the goal of the paper, the dataset on business clients of several banks in Croatia, which, besides size, incorporates a number of different criteria, is analyzed using the SOM-Ward clustering algorithm of Viscovery SOMine software. The SOM-Ward algorithm extracted three segments that differ with respect to the attributes of foreign trade operations (import/export), annual income, origin of capital, important bank selection criteria, views on the loan selection and the industry. The analyzed segments can be used by banks for deciding on the direction of further marketing activities.

scholarly journals Using Self-Organizing Maps for Rural Territorial Typology

2011 ◽  
pp. 107-126 ◽  
Author(s):  
Marcos Santos da Silva ◽  
Edmar Ramos de Siqueira ◽  
Olívio Teixeira ◽  
Maria Manos ◽  
Antônio Monteiro
Keyword(s):  
Neural Network ◽  
Temporal Analysis ◽  
The Self ◽  
Self Organizing Map ◽  
Clustering Methods ◽  
Self Organizing Maps ◽  
Policy Makers ◽  
Spatial Temporal Analysis ◽  
Self Organizing

This work assessed the capacity of the self-organizing map, an unsupervised artificial neural network, to aid the process of territorial design through visualization and clustering methods applied to a multivariate geospatial temporal dataset. The method was applied in the case study of Sergipe‘s institutional regional partition (Territories of Identity). Results have shown that the proposed method can improve the exploratory spatial-temporal analysis capacity of policy makers that are interested in territorial typology. A new partition for rural planning was elaborated and confirmed the coherence of the Territories of Identity.

Assessment of Clusteranalysis and Self-Organizing Maps

1998 ◽  
Vol 06 (02) ◽  
pp. 139-149 ◽  
Author(s):  
Helge Petersohn
Keyword(s):  
Neural Networks ◽  
Market Segmentation ◽  
Clustering Methods ◽  
Clustering Method ◽  
Self Organizing Maps ◽  
Methodical Approach ◽  
Starting Conditions ◽  
Marketing Activities ◽  
Special Approach ◽  
Self Organizing

Market segmentation represents a central problem of preparing marketing activities. The methodical approach of this problem is supported by clustering methods. Available data are used to detect common grounds regarding their quality structures. Therefore statistics provides various methods for cluster analysis. Self-organizing maps are another possibility to form classes. They are a special approach of the artificial neural networks. The statistical methods and these methods, which are based on organic processes of our brain, offer different solutions although the starting conditions are the same. Often decisions about investigations are based on such solutions. Therefore the results of clustering are very important to reveal systematic information about the size of classes and their structure. Methodical notes are needed for the use of any clustering method. This paper offers a simplified way to select the best result for clustering.

scholarly journals quicksom: Self-Organizing Maps on GPUs for clustering of molecular dynamics trajectories

2020 ◽  
Author(s):  
Vincent Mallet ◽  
Michael Nilges ◽  
Guillaume Bouvier
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
The Self ◽  
Use Case ◽  
Clustering Methods ◽  
Self Organizing Maps ◽  
Molecular Conformations ◽  
Dynamics Simulations ◽  
Self Organizing

Abstract Summary We implemented the Self-Organizing Maps algorithm running efficiently on GPUs, and also provide several clustering methods of the resulting maps. We provide scripts and a use case to cluster macro-molecular conformations generated by molecular dynamics simulations. Availability and implementation The method is available on GitHub and distributed as a pip package.

The self-organizing map in synoptic climatological research

2011 ◽  
Vol 35 (1) ◽  
pp. 109-119 ◽  
Author(s):  
Scott C. Sheridan ◽  
Cameron C. Lee
Keyword(s):  
Spatial Patterns ◽  
The Self ◽  
Synoptic Climatology ◽  
Self Organizing Map ◽  
Two Dimensional ◽  
Clustering Methods ◽  
Traditional Methods ◽  
Self Organizing Maps ◽  
Dimensional Array ◽  
Self Organizing

Self-organizing maps (SOMs) are a relative newcomer to synoptic climatology; the method itself has only been utilized in the field for around a decade. In this article, we review the major developments and climatological applications of SOMs in the literature. The SOM can be used in synoptic climatological analysis in a manner similar to most other clustering methods. However, as the results from a SOM are generally represented by a two-dimensional array of cluster types that ‘self-organize’, the synoptic categories in the array effectively represent a continuum of synoptic categorizations, compared with discrete realizations produced through most traditional methods. Thus, a larger number of patterns can be more readily understood, and patterns, as well as transitional nodes between patterns, can be discerned. Perhaps the most intriguing development with SOMs has been the new avenues of visualization; the resultant spatial patterns of any variable can be more readily understood when displayed in a SOM. This improved visualization has led to SOMs becoming an increasingly popular tool in various research with climatological applications from other disciplines as well.

scholarly journals Analisis strategi dan klasterisasi ketahanan pangan nasional dalam menghadapi pandemi covid-19

2021 ◽  
Vol 12 (1) ◽  
pp. 110-122
Author(s):  
Audi Ramadhan ◽  
Kinanti Prawita ◽  
M. Andik Izzudin ◽  
Gitta Amandha
Keyword(s):  
Food Security ◽  
Policy Decision ◽  
Security Level ◽  
Clustering Methods ◽  
Self Organizing Maps ◽  
Regional Food ◽  
Silhouette Coefficient ◽  
Result Show ◽  
Security Index ◽  
Self Organizing

Covid-19 outbreak that scours the world nowadays is affecting all sectors, including food security. Therefore it needs to restructuring the food security policies to ensure that every people obtains adequate and nutritious food. However, the society in each province have different conditions. Thus the clusterization of food security level per province is indispensable to support strategic and policy decision in order to face the Covid-19 pandemic. This research aimed to clustering food security level of each province in Indonesia. Furthernore, this research also compare several clustering methods. The clustering method that used as a comparison in this study is K-means, DBSCAN, Louvain and Self organizing maps methods. Method with the highest silhouette coefficient value in this research will represent the results of food security clustering. The resul of the research show that K-means achieve highest silhouette coefficient value (0.568). Therefore the clusterization result of K-means chosen to represent the level of food security in this research. Further, it followed by self organizing maps with silhouette coefficient 0.559, louvain 0.312 and DBSCAN 0.15. The clusterization result show there are 7 provinces with high food security index, 24 provinces with medium food security index and 3 provinces with low food security index. This research also propose policies strategy and recommendation related to regional food security condition in order to face the Covid-19 pandemic. This research is expected to be a consideration of Indonesian government in making policies on national food security.

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