• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.167-172
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• 2007

Long gene sequences and their products have been studied by many methods. The use of DNA(Deoxyribonucleic acid) microarray technology has resulted in an enormous amount of data, which has been difficult to analyze using typical research methods. This paper proposes that mass data be analyzed using division clustering with the K-means clustering algorithm. To demonstrate the superiority of the proposed method, it was used to analyze the microarray data from rice DNA. The results were compared to those of the existing K-meansmethod establishing that the proposed method is more useful in spite of the effective reduction of performance time.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.343-352
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• 2000

In this study. the author proposes a nonhierarchical clustering method. called the "Double K-Means Clustering", which performs clustering of multivariate observations with the following algorithm: Step I: Carry out the ordinary K-means clmitering and obtain k temporary clusters with sizes $n_1$,... , $n_k$, centroids $c_$1,..., $c_k$ and pooled covariance matrix S. $\bullet$ Step II-I: Allocate the observation x, to the cluster F if it satisfies ..... where N is the total number of observations, for -i = 1, . ,N. $\bullet$ Step II-2: Update cluster sizes $n_1$,... , $n_k$, centroids $c_$1,..., $c_k$ and pooled covariance matrix S. $\bullet$ Step II-3: Repeat Steps II-I and II-2 until the change becomes negligible. The double K-means clustering is nearly "optimal" under the mixture of k multivariate normal distributions with the common covariance matrix. Also, it is nearly affine invariant, with the data-analytic implication that variable standardizations are not that required. The method is numerically demonstrated on Fisher's iris data.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.281-288
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• 2014

A method for edge detection using K-means clustering is proposed in this paper. The method is performed through there steps. Histogram equalizing is applied to the image for the uniformed intensity distribution. Pixels are clustered by K-means clustering technique. Then Sobel mask is applied to detect edges. Experiments showed that this method detected edges better than conventional method.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.3
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• pp.205-209
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• 2004

The K-means algorithm is the most widely used method among the codebook generation algorithms in vector quantization. In this paper, we propose a codebook generation algorithm using a new updating condition to enhance the codebook performance. The conventional K-means algorithm uses a fixed weight of the distance for all training iterations, but the proposed method uses different weights according to the updating condition from the new codevectors for training iterations. Then, different weights can be applied to generate codevectors at each iteration according to this condition, and it can have a similar effect to variable weights. Experimental results show that the proposed algorithm has the better codebook performance than that of K-means algorithm.

• Journal of the Korea Safety Management & Science
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• v.25 no.1
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• pp.31-38
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• 2023

The object of this study is to evaluate whether the means of egress of Jechon Sports Center and Miryang Sejong Hospital, where massive fire human casualties occurred in 2017 and 2018 respectively, comply with NFPA 101(Life Safety Code), and to suggest the need for supplementation of domestic means of egress regulations. The study evaluated the number and arrangement of the means of egress, travel distance, common path of travel, dead end and discharge from exit for each building by applying the means of egress regulations of NFPA 101. As a result of the evaluation through NFPA 101, the travel distance was appropriate, but some of the other items except for the travel distance did not meet NFPA 101. The regulations that need to be supplemented are 1)occupant load calculation 2)egress capacity calculation 3)continuous concept of means of egress 4)concept of common path of travel. It is especially necessary to revise the requirement for fire door of the evacuation floor(normal 1^st floor) of the stairwell in case of below the five story building.

• The Journal of Society for e-Business Studies
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• v.7 no.3
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• pp.49-60
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• 2002

Many researchers have studied component technologies with concept, methodology and implementation for partial business domain, however there are rarely researches for component classification to manage these systematically. In this paper, we suggest a component classification model, which can make component reusability higher and can derive higher productivity of software development. We take four focuses generalization, abstraction, technology and size. The generalization means which category a component belongs to. The abstraction means how specific a component encapsulates its inside. The technology means which platform for hardware environment a component can be plugged in. The size means the physical component volume.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.301-309
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• 2006

In this paper, we show the improved fuzzy c-means clustering method. To improve, we use the double clustering as principal component analysis from objects which is located on common region of more than two clusters. In addition we use the degree of membership (probability) of fuzzy c-means which is the advantage. From simulation result, we find some improvement of accuracy in data of the probability 0.7 exterior and interior of overlapped area.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.619-631
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• 2006

When X and Y have independent Poisson distributions, we develop a Bayesian one-sided testing procedures for the ratio of two Poisson means. We propose the objective Bayesian one-sided testing procedures for the ratio of two Poisson means based on the fractional Bayes factor and the intrinsic Bayes factor. Some real examples are provided.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1387-1395
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• 2006

This paper considers testing for the ratio of two exponential means. We propose a solution based on a Bayesian decision rule to this problem in which no subjective input is considered. The criterion for testing is the Bayesian reference criterion (Bernardo, 1999). We derive the Bayesian reference criterion for testing the ratio of two exponential means. Simulation study and a real data example are provided.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.671-672
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• 2008

K-means is a popular one in clustering algorithms, and it minimizes the mutual euclidean distance among the sample points. But K-means has some demerits, such as depending on initial condition, unsupervised learning and local optimum. However mahalanobis distancecan deal this case well. In this paper, the author proposed a new clustering algorithm, named exponential probability clustering, which applied Mahalanobis distance into K-means clustering. This new clustering does possess not only the probability interpretation, but also clustering merits. Finally, the simulation results also demonstrate its good performance compared to K-means algorithm.