• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.83-98
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• 2005

Exploring distributional patterns of multivariate data is very essential in understanding the characteristics of given data set, as well as in building plausible models for the data. For that purpose, low-dimensional visualization methods have been developed by many researchers along various directions. As one of methods, Kohonen's SOM (Self-Organizing Map) is prominent. SOM compresses the volume of the data, yields abstraction from the data and offers visual display on low-dimensional grids. Although it is proven quite effective, it has one undesirable property: SOM's display is discrete. In this study, we propose two techniques for enhancing quality of SOM's display, so that SOM's display becomes continuous. The proposed methods are demonstrated in two numerical examples.

• The Transactions of the Korea Information Processing Society
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• v.7 no.10
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• pp.3098-3104
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• 2000

This paper defiens a perceptron-like self-organizing map(PSOM) and show that PSOM is equivalent to Kohonen's self-organizing map(SOM) if target values of output neurons of PSOM are selected properly. This fact imphes that PSOM is a generalized SOM algorithm. This paper also show that if clustering is restricted to vector sets distributed on hypersphere with unit radius, SOM and dot-product SOM(DOSM) are equivalent algorithms. Therefore we conclude that DSOM is a special case of SOM, which in turn a special, case of PSOM.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.251-267
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• 2002

Self Organizing Maps(SOM) by Kohonen is very fast algorithm in neural networks. But it doesn't show sure rules of training results. In this paper, we introduce to Bayesian Learning for Self Organizing Maps(BLSOM) which combines self organizing maps with Bayesian learning. So it supports explanatory power of models and improves prediction. BLSOM has global optima anywhere but SOM has not. This is proved by experiment in this paper.

• Atmosphere
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• v.21 no.3
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• pp.221-228
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• 2011

The nonlinear characteristics of summer monsoon intraseasonal oscillation (ISO) in precipitation, which is manifested as fluctuations in convection and circulation, is one of the major difficulty on the prediction of East Asian summer monsoon (EASM). The present study aims to identify the spatial distribution and time evolution of nonlinear phases of monsoon ISO. In order to classify the different phases of monsoon ISO, Self-Organizing Map(SOM) known as a nonlinear pattern recognition technique is used. SOM has a great attractiveness detecting self-similarity among data elements by grouping and clustering such self-similar components. The four important patterns are demonstrated as Meiyu-Baiu, Changma, post-Changma, and dry-spell modes. It is found that SOM well captured the formation of East Asian monsoon trough during early summer and its northward migration together with enhanced convection over subtropical western Pacific and regionally intensive precipitation including Meiyu, Changma and Baiu. The classification of fundamental large scale spatial pattern and evolutionary history of nonlinear phases of monsoon ISO provides the source of predictability for extended-range forecast of summer precipitation.

• Journal of the Korean Society for information Management
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• v.24 no.3
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• pp.321-341
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• 2007

It will be possible to prevent the infringement of the trademarks and the insueing disputes regarding the originality of the trademarks by using an efficient content-based trademark image retrieval system. In this paper, we describe a content-based image retrieval system using the Self Organizing Map(SOM) algorithm. The SOM algorithm utilizes the visual features, which were derived from the gray histogram representation of the images. In addition, we made the objective effectiveness evaluation possible by coming up with a quantitative measure to gauge the effectiveness of the content-based image retrieval system.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.439-448
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• 2006

Previous recognition/clustering algorithms such as Kohonen SOM(Self-Organizing Map), MLP(Multi-Layer Percecptron) and SVM(Support Vector Machine) might not adapt to unexpected input pattern. And it's recognition rate depends highly on the complexity of own training patterns. We could make up for and improve the weak points with lowering complexity of original problem without losing original characteristics. There are so many ways to lower complexity of the problem, and we chose a kernel concepts as an approach to do it. In this paper, using a kernel concepts, original data are mapped to hyper-dimension space which is near infinite dimension. Therefore, transferred data into the hyper-dimension are distributed spasely rather than originally distributed so as to guarantee the rate to be risen. Estimating ratio of recognition is based on a new similarity-probing and learning method that are proposed in this paper. Using CEDAR DB which data is written in cursive letters, 0 to 9, we compare a recognition/clustering performance of kSOM that is proposed in this paper with previous SOM.

• The Journal of Korean Association of Computer Education
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• v.9 no.5
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• pp.77-84
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• 2006

In this paper, we propose an adaptive image watermarking algorithm in DWT domain by using HVS(human Visual system) and SOM(Self-Organizing Map) among neural networks. HVS can be described in terms of two properties of HVS: brightness and texture sensitivity. The SOM is used to obtain the local characteristics of image, Therefore, the suitable strength and length of embedded watermark is determined by using HVS and SOM. The experimental results show that proposed method provides a suitable strength and length of watermark and has good perceptual invisibility and robustness for various attacks.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1056-1059
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• 2003

Self-Oranizing Map(SOM) is an unsupervised neural network providing cluster analysis of high dimensional input data. The output from the SOM is represented in map that help us to explore data. The weak point of conventional SOM is when the map is large, it take a long time to train the data. The computing time is known to be O(MN) for trainning to find the winning node (M,N are the number of nodes in width and height of the map). This paper presents a new method to reduce the computing time by creating new map. Each node in a new map is the centroid of nodes' group that are in the original map. After create a new map, we find the winning node of this map, then find the winning node in original map only in nodes that are represented by the winning node from the new map. This new method is called "High Speed Self-Oranizing Map"(HS-SOM). Our experiment use HS-SOM to cluster documents and compare with SOM. The results from the experiment shows that HS-SOM can reduce computing time by 30%-50% over conventional SOM.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.995-1007
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• 2008

Studies on modeling the rainfall-runoff relationship which shows nonlinear trend strongly use artificial neural networks theory not only for the prediction but also for the characteristics analysis of the data used by pattern classification. For the pattern classification, the results from Self-Organizing Map (SOM) mention that the map size and array for the SOM training have significantly influenced on the SOM performance. Since there is no deterministic method or theoretical equation to determine the number of rows and columns for the map size, hexagonal array is generally used for the map array. Therefore, this study present a determination of the optimized map structure for the rainfall-runoff analysis in Naju station considering the map size and array simultaneously which can represent the classified characterization of rainfall-runoff relationship. The result showed that the map size of 20$\times$16 hexagonal array with 8-clustered patterns was selected as an appropriate map structure for rainfall-runoff analysis in Naju station.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.116-120
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• 2008

Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.