• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.158-161
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• 2004

Fuzzy Kernel C-Means(FKCM) 알고리즘은 커널 함수를 통하여 구형의 데이터뿐만 아니라 Fuzzy C-Means(FCM)에서는 분류하기 힘든 복잡한 형태의 분포를 갖는 데이터를 분류할 수 있다. 하지만 FCM과 같이 노이즈에 대해서는 민감한 성질을 가진다 이처럼 노이즈(noise)에 민감한 성질을 보완하기 위해서 본 논문에서는 Possibllistic C-Means 알고리즘에 커널 함수를 적용하였다. 본 논문에서 제안된 Kernel Possibilistic C-Means(KPCM) 알고리즘은 일반적인 데이터에 대해 FKCM과 같은 성능의 클러스터링 수행이 가능하며 노이즈가 있는 데이터에 대해서는 FKCM보다 더욱 정확한 클러스터링을 수행할 수 있다.

• 한국데이터정보과학회:학술대회논문집
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• 2004.10a
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• pp.77-86
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• 2004

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 analyze 2002 Gyeongnam social indicator survey data using k-means clustering techniques for environmental information. We can use these outputs given by k-means clustering for environmental preservation and environmental improvement.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.109-112
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• 2001

기존의 K-means 알고리즘은 학습벡터가 단일군집에 할당되는 방법이 crisp 이므로 다른 군집에 할당될 확률을 무시하게 된다. 따라서 군집화 작업과 관련하여 반복적인 코드북 설계 과정에서 각 학습벡터를 다중 군집으로 할당하는 Fuzzy c-means를 사용한다. 또한 Fuzzy c-means 알고리즘의 학습과정에서 구해지는 각 클래스 의 프로토타입에 가중치를 곱하여 다음 학습의 프로토타입으로 사용함으로써 Fuzzy c-means 알고리즘 적용 결과 얻어지는 코트북의 성능을 기존 알고리즘과 비교하여 개선된 Fuzzy c-means 알고리즘을 찾기 위한 근거를 마련한다.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.113-116
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• 2001

The conventional K-means algorithm is widely used in vector quantizer design and clustering analysis. Recently modified K-means algorithm has been proposed where the codevector updating step is as fallows: new codevector = current codevector + scale factor (new centroid - current codevector). This algorithm uses a fixed value for the scale factor. In this paper, we propose a new algorithm for the enhancement of learning time in fuzzy c-means a1gorithm. Experimental results show that the proposed method produces codebooks about 5 to 6 times faster than the conventional K-means algorithm with almost the same Performance.

• Journal of Korean Medical classics
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• v.13 no.1
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• pp.17-43
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• 2000

This study on the conception of Pyo Bon expressed in Hwang Jae Nai Kyung was summarized as follows: 1. The conception of Pyo Bon in the Yi Jung Byun Gi Lon(移精變氣論) and Tang Eak Yo Le Lon(湯液료醴論) of So Moon(素問) is that Pyo means the doctor and Bon means the disease. The Pyo Bon of this chapter has a meaning of time, namely first and last. 2. The conception of Pyo Bon in the Soo Yul Hyul Lon(水熱穴論) of So Moon is that Pyo means the lung and Bon means the kidney. The Pyo Bon of this chapter has a meaning of space, namely the upper and lower sides. 3. The conception of Pyo Bon in the Pyo Bon Byung Jun Lon(標本病傳論) of So Moon is that Pyo means a earlier disease and Bon means a later disease. The Pyo Bon of this chapter has a meaning of time, namely first and last. 4. The conception of Pyo Bon in the Chun Won Gi Dae Lon(天元紀大論), Yug Mi Ji Dae Lon(六微旨大論) and Ji Jin Yo Dae Lon(至眞要大論) of So Moon is that Pyo means a Yug Gi(六氣), namely wind, cold, heat, dampness, dryness, fire and Bon means a Sam Eum Sam Yang(三陰三陽), The Pyo Bon of this chapter includes a meaning of time and space. 5. The conception of Pyo Bon in the Sa Jun(師傳) of Yung Chu(靈樞) is that Pyo means a inside of the body and Bon means a outside of the body. The Pyo Bon of this chapter a meaning of space, namely the inside and outside. 6. The conception of Pyo Bon in the Wi Gi(衛氣) of Yung Chu is that Pyo means the end of limbs and Bon means the part of head, face, chest, abdomen, back. The Pyo Bon of this chapter has a meaning of space, namely center and circumference.

• Journal of Convergence for Information Technology
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• v.8 no.5
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• pp.85-93
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• 2018

This study investigates the suitability of the blockchain and FIDO among non-face-to-face authentication means in order to secure diversity of identfication means operated in South Korea. In order to do this, the study selected and analyzed seven conformance criteria (universality, persistence, uniqueness, convenience, security, applicability, and economics), and the results were appropriate. Accordingly, in order to apply the blockchain and FIDO as the identification means, the related regulations and notices should be revised to improve the identification procedure. In addition, differentiated certification standards should be established for each service field to apply various authentication means as well as existing identification means, and the authentication means should be continuously developed and linked with the service. In the future, the identification means will bring security of the information circulation environment in the IoT, so it should be implemented in a variety of services by supporting application of identification means.

• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.4
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• pp.452-459
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• 1998

The purpose of this study was to discover a conceptual framework and evaluation process of apparel quality by means-end chain theory. The theoretical study was conducted to find out a conceptual framework and build a hypothetical evaluation process model of apparel quality. Apparel quality was perceived associative network called a means-end chain and was evaluated in several stages. A conceptual framework of apparel quality evaluation was organized into hierarchical relationships among four different dimensions: physical attribute, physical function, instrumental performance, and expressive performance. The means-end structure linked tangible physical attributes and function to more abstract instrumental and expressive performance. A hypothetical evaluation process model linked dimensions of apparel quality to the selected means-end relationship. Different consumers had different means-end chains for the same apparel. Therefore different subjects are likely to have different evaluation paths. From this study we can suggest an evaluation process model of apparel quality.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.471-483
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• 2012

One of the most important problems in cluster analysis is the selection of variables that truly define cluster structure, while eliminating noisy variables that mask such structure. Brusco and Cradit (2001) present VS-KM(variable-selection heuristic for K-means clustering) procedure for selecting true variables for K-means clustering based on adjusted Rand index. This procedure starts with the fixed number of clusters in K-means and adds variables sequentially based on an adjusted Rand index. This paper presents an updated procedure combining the VS-KM with the automated K-means procedure provided by Kim (2009). This automated variable selection procedure for K-means clustering calculates the cluster number and initial cluster center whenever new variable is added and adds a variable based on adjusted Rand index. Simulation result indicates that the proposed procedure is very effective at selecting true variables and at eliminating noisy variables. Implemented program using R can be obtained on the website "http://faculty.knou.ac.kr/sskim/nvarkm.r and vnvarkm.r".

• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.9-14
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• 2015

In this paper, we present an color quantization method by complementing the disadvantage of K-means color quantization that is one of the well-known color quantization. We named the proposed method "octree-means" color quantization. K-means color quantization does not use all of the clusters because it initializes the centroid of clusters with random value. The proposed method complements this disadvantage by using the octree color quantization which is fast and uses the distribution of colors in image. We compare the proposed method to six well-known color quantization methods on ten test images to evaluate the performance. The experimental results show 68.29 percent of mean square error(MSE) and processing time increased by 14.34 percent compared with K-means color quantization. Therefore, the proposed method improved the K-means color quantization and perform an effective color quantization.

• Journal of information and communication convergence engineering
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• v.6 no.4
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• pp.434-438
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• 2008

Massive data are continuously produced with a data rate of over several terabytes every day. These applications need effective clustering algorithms to achieve an overall high performance computation. In this paper, we propose ancestor as cluster center based approach to clustering, the K-means algorithm using ancestor. We modify the K-means algorithm. We present a clustering architecture and a clustering algorithm that minimize of I/Os and show a performance with excellent. In our experimental performance evaluation, we present that our algorithm can improve the I/O speed and the query processing time.