• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.185-192
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• 2005

There are many data mining techniques such as association rule, decision tree, neural network analysis, clustering, genetic algorithm, bayesian network, memory-based reasoning, etc. We analyze 2003 Gyeongnam social indicator survey data using k-means clustering technique for environmental information. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper, we used k-means clustering of several clustering techniques. The k-means clustering is classified as a partitional clustering method. We can apply k-means clustering outputs to environmental preservation and environmental improvement.

• Korean Journal of Microbiology
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• v.15 no.1
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• pp.42-45
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• 1977

Bdellovibrio sp. is an ectoparasitic bacteria which is predatory and parasitic upon other bacteria. This study was carried out the isolation of Bdellovibrio sp. from several soil smaples and observation of this organisms by means of electron microscope. The results are as follows ; The primary isolated Bdellovibrio sp. from soil is an obligate intracellular rod form parasite and possess a monoflagella.

• Kyungpook Mathematical Journal
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• v.14 no.2
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• pp.185-194
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• 1974

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.11-18
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• 2010

Fuzzy c-means (FCM) is a simple but efficient clustering algorithm using the concept of a fuzzy set that has been proved to be useful in many areas. There are, however, several well known problems with FCM, such as sensitivity to initialization, sensitivity to outliers, and limitation to convex clusters. In this paper, global fuzzy c-means (G-FCM) and kernel fuzzy c-means (K-FCM) are combined to form a non-linear variant of G-FCM, called kernel global fuzzy c-means (KG-FCM). G-FCM is a variant of FCM that uses an incremental seed selection method and is effective in alleviating sensitivity to initialization. There are several approaches to reduce the influence of noise and accommodate non-convex clusters, and K-FCM is one of them. K-FCM is used in this paper because it can easily be extended with different kernels. By combining G-FCM and K-FCM, KG-FCM can resolve the shortcomings mentioned above. The usefulness of the proposed method is demonstrated by experiments using artificial and real world data sets.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.368-374
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• 2010

Image segmentation is the basic step in the field of the image processing for pattern recognition, environment recognition, and context analysis. The Otsu's automatic threshold selection, which determines the optimal threshold value to maximize the between class scatter using the distribution information of the normalized histogram of a image, is the famous method among the various image segmentation methods. For the automatic threshold selection proposed by Otsu, it is difficult to determine the optimal threshold value by considering the sub-region characteristic of the image because the Otsu's algorithm analyzes the global histogram of a image. In this paper, to alleviate this difficulty of Otsu's image segmentation algorithm and to improve image segmentation capability, the original image is divided into several sub-images by using context fuzzy c-means algorithm. The proposed fuzzy Otsu threshold algorithm is applied to the divided sub-images and the several threshold values are obtained.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.889-894
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• 2004

The fuzzy kernel c-means (FKCM) algorithm, which uses a kernel function, can obtain more desirable clustering results than fuzzy c-means (FCM) for not only spherical data but also non-spherical data. However, it can be sensitive to noise as in the FCM algorithm. In this paper, a kernel function is applied to the possibilistic c-means (PCM) algorithm and is shown to be robust for data with additive noise. Several experimental results show that the proposed kernel possibilistic c-means (KPCM) algorithm out performs the FKCM algorithm for general data with additive noise.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.81-86
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• 2012

In this paper, we proposed a new design methodology of a pattern classification rule based on the local linear discriminant analysis expanded from the generic linear discriminant analysis which is used in the local area divided from the whole input space. There are two ways such as k-Means clustering method and the differential evolutionary algorithm to partition the whole input space into the several local areas. K-Means clustering method is the one of the unsupervised clustering methods and the differential evolutionary algorithm is the one of the optimization algorithms. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods.

• Journal of Engineering Education Research
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• v.8 no.1
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• pp.31-45
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• 2005

In this paper, selecting survey items was performed using three clustering methods: factor analysis, fuzzy c-Means algorithm and cluster analysis. The methods were used to extract key items from various questionnaires. The key item represents several similar questionnaires that form a cluster. Test survey was made of 120 items obtained from several surveys and it was answered by 646 students from 4 universities. Each item contains 6 choices. Applying the clustering method chose 25 items which is reduced from the original 120 items. The results yielded by three methods are very similar.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.605-608
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• 1993

Breakdown characteristics of a small rod-to-rod microairgap has been studied for obtain an optimum breakdown voltage and an airgap spacing to be used as an emission control means by the electrical arc-burning unburnt carbon particulates exhausted from a power station burner. It is found that the breakdown voltage at the rod-to-rod airgap spacing in the rang of $1{\sim}100{\mu}m$ decreased with decrease in the rod-to-rod airgap spacing. And there were no minimum breakdown voltage on a $V_b$-Pd characteristics which is known as the minimum voltage in Paschen's law in air atmosphere. Breakdown voltages of the airgap at the constant airgap spacing were $V_{b-dc}>V_{b-ac}>V_{b-pulse}$, and it was lowest for the pulse voltage applied. As a result, it is found that a pulse power was one of effective power compared with dc or ac to be used as such an unburnt carbon particulate emission control means and the airgap spacing became to several tens ${\mu}m$, then the breakdown voltages were down to several handreds voltages.

• Journal of the Korea Society for Simulation
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• v.3 no.2
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• pp.27-36
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• 1994

In many real-world simulation studies the several measures of performance are of interest simultaneously. There exist very limited number of studies that explain and suggest the methods or procedures of inferencing the system performances at the same time. This study presents a procedure for determining the number of simulation observations required to achieve the prespecified confidence level for several measures of system performance. Mean values are selected as the measures, for instance, expected ordering cost, expected holding cost, and expected shortage cost for a given period of time in the study of inventory problems. Basically, the batch means approach is applied and extended to develop an algorithm to carry out the procedure handling more than single parameter. The efficacy of the presented method is assessed through the experiments. The empirical results based on some stochastic systems such as queues and inventory problems show that the suggested method produces as excellent result in terms of the precision of estimated means and the number of observations required.