• Korean Journal of Computational Design and Engineering
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• v.14 no.6
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• pp.382-389
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• 2009

Recently, knowledge management has been required in companies as a tool of competitiveness. Companies have constructed Enterprise Resource Planning(ERP) system in order to manage huge knowledge. But, it is not easy to formalize knowledge in organization. We focused on data mining system by genetic programming(GP). Data mining system by genetic programming can be useful tools to derive and extract the necessary information and knowledge from the huge accumulated data. However when we don't have enough amounts of data to perform the learning process of genetic programming, we have to reduce input parameter(s) or increase number of learning or training data. In this study, an enhanced data mining method combining Genetic Programming with Self organizing map, that reduces the number of input parameters, is suggested. Experiment results through a prototype implementation are also discussed.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.223-230
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• 2003

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e 'g', documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(e'g', R- Tree family, K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R＊-tree is the most successful variant of the R-tree. In this paper, we propose a SOM-based R＊-tree as a new indexing method for high-dimensional feature vectors.The SOM-based R＊-tree combines SOM and R＊-tree to achieve search performance more scalable to high dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. A best-matching-image-list. (BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. When we build an R＊-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SOM-based R＊-tree with that of an SOM and an R＊-tree using color feature vectors extracted from 40, 000 images. The result show that the SOM-based R＊-tree outperforms both the SOM and R＊-tree due to the reduction of the number of nodes required to build R＊-tree and retrieval time cost.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.117-135
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• 2003

This study is to propose a procedure solving scale problem of traditional collaborative filtering (CF) approach. The CF approach generally uses some similarity measures like correlation coefficient. So, as the user of the Website increases, the complexity of computation increases exponentially. To solve the scale problem, this study suggests a clustering model-based approach using Self-Organizing Map (SOM) and RFM (Recency, Frequency, Momentary) method. SOM clusters users into some user groups. The preference score of each item in a group is computed using RFM method. The items are sorted and stored in their preference score order. If an active user logins in the system, SOM determines a user group according to the user's characteristics. And the system recommends items to the user using the stored information for the group. If the user evaluates the recommended items, the system determines whether it will be updated or not. Experimental results applied to MovieLens dataset show that the proposed method outperforms than the traditional CF method comparatively in the recommendation performance and the computation complexity.

• Journal of information and communication convergence engineering
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• v.8 no.6
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• pp.609-617
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• 2010

This paper is concerned with the use of a selforganizing map (SOM) to estimate the desired channel states of an unknown digital communication channel for blind equalization. The modification of SOM is accomplished by using the Bayesian likelihood fitness function and the relation between the desired channel states and channel output states. At the end of each clustering epoch, a set of estimated clusters for an unknown channel is chosen as a set of pre-defined desired channel states, and used to extract the channel output states. Next, all of the possible desired channel states are constructed by considering the combinations of extracted channel output states, and a set of the desired states characterized by the maximal value of the Bayesian fitness is subsequently selected for the next SOM clustering epoch. This modification of SOM makes it possible to search the optimal desired channel states of an unknown channel. In simulations, binary signals are generated at random with Gaussian noise, and both linear and nonlinear channels are evaluated. The performance of the proposed method is compared with those of the "conventional" SOM and an existing hybrid genetic algorithm. Relatively high accuracy and fast search speed have been achieved by using the proposed method.

• Journal of Korean Society on Water Environment
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• v.24 no.5
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• pp.611-617
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• 2008

It is necessary to analysis the data characteristics of discharge and water quality for efficient water resources management, aggressive alternatives to inundation by flood and various water pollution accidents, the basic information to manage water quality in lakes and to make environmental policy. Therefore, the present study applied Self-Organizing Map (SOM) showing excellent performance in classifying patterns with weights estimated by self-organization. The result revealed five patterns and TOC versus rainfall-storage data according to the respective patterns were depicted in two-dimensional plots. The visualization presented better understanding of data distribution pattern. The result in the present study might be expected to contribute to the modeling procedure for data prediction in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.402-405
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• 2000

In this paper we propose a method ef pattern classification of the hand movement using EMG signals through Self-organizing feature map. Self-organizing feature map is an artificial neural network which organizes its output neuron through leaning and therefore it can classify input patterns. The raw EMC signals become direct input to the Self-organizing feature map. The simulation and experiment results showed the effectiveness of the classification of EMG signal using the Self-organizing feature map.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.50-56
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• 2011

This paper proposes 3D visualization method of compound knowledge which will be able to identify and search easily compound knowledge objects based the multidimensional relationship. For this, we structurized a compound knowledge with link and node which become the semantic network. and we suggested 3D visualization method using SOM. Also, to arrange compound knowledge from 3D space and to provide the chance of realistic and intuitional information retrieval to the user, we proposed compound knowledge 3D clustering methods using object similarity. Compound knowledge 3D visualization and clustering using SOM will be the optimum method to appear context of compound knowledge and connectivity in space-time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4B
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• pp.389-398
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• 2006

The present study compositely applied the self-organizing map (SOM), which is a kind of artificial neural networks (ANNs), and the back propagation algorithm (BPA) for the rainfall-runoff prediction model taking account of the irregular variation of the spatiotemporal distribution of rainfall. To solve the problems from the previous studies on ANNs, such as the overestimation of low flow during the dry season, the underestimation of runoff during the flood season and the persistence phenomenon, in which the predicted values continuously represent the preceding runoffs, we introduced SOM theory for the preprocessing in the prediction model. The theory is known that it has the pattern classification ability. The method proposed in the present research initially includes the classification of the rainfall-runoff relationship using SOM and the construction of the respective models according to the classification by SOM. The individually constructed models used the data corresponding to the respectively classified patterns for the runoff prediction. Consequently, the method proposed in the present study resulted in the better prediction ability of runoff than that of the past research using the usual application of ANNs and, in addition, there were no such problems of the under/over-estimation of runoff and the persistence.

• Korean Journal of Environmental Agriculture
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• v.34 no.3
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• pp.168-177
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• 2015

BACKGROUND: In recent year, there has been great concern regarding agricultural land uses and their importance for the conservation of biodiversity. Rice fields are managed unique wetland for wildlife, especially waterbirds. A comprehensive monitoring of the waterbird assemblage to understand patterning changes was attempted for rice ecosystem in South Korea. This rice ecosystem has been recognized as one of the most important for waterbirds conservation. METHODS AND RESULTS: Biweekly monitoring was implemented for the 4 years from April 2009 to March 2010, from April 2011 to March 2014. 32 species of waterbirds were observed. Self-organizing map (SOM) and random forest were applied to the waterbirds dataset to identify the characteristics in waterbirds distribution. SOM and random forest analysis clearly classified into four clusters and extract ecological information from waterbird dataset. Waterbird assemblages represented strong seasonality and habitat use according to waterbird group such as shorebirds, herons and waterfowl. CONCLUSION: Our results showed that the combination of SOM and random forest analysis could be useful for ecosystem assessment and management. Furthermore, we strongly suggested that a strict management strategy for the rice fields to conserve the waterbirds. The strategy could be seasonally and species specific.

• Korean Management Science Review
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• v.21 no.3
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• pp.85-99
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• 2004

The retention of customer is an increasingly pressing issue in today's competitive environment. The proposes of this paper is a personalized defection detection and the procedure of prevention based on economic analysis of customer defection possibility, and behaviour state transition cost. This procedure is based on the observation that potential defectors have a tendency to take a couple of months or weeks to gradually change their behaviour before their eventual withdrawal. In this procedure, the SOM(Self-Organizing Map) is used to determine the possible states of customer behaviour from past behaviour data, and to prevent the defection of potential defectors, the proposed procedure recommends the desirable behaviour state for the next period based on the analysis of transition cost. and likelihood of defection. The case study has been conducted for a Korean on-line game provider to evaluate of this procedure.