• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.717-718
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• 2008

Fisher Linear Discriminant(FLD) is a sample and intuitive linear feature extraction method in pattern recognition. But in some special cases, such as un-separable case, one class data dispersed into several clustering case, FLD doesn't work well. In this paper, a new discriminant named K-means Fisher Linear Discriminant, which combines FLD with K-means clustering is proposed. It could deal with this case efficiently, not only possess FLD's global-view merit, but also K-means' local-view property. Finally, the simulation results also demonstrate its advantage against K-means and FLD individually.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.88-88
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• 2000

As more and more critical commercial applications move on the Internet, providing highly available servers becomes increasingly important. One of the advantages of a clustered system is that it has hardware and software redundancy. High availability can be provided by detecting node or daemon failure and reconfiguring the system appropriately so that the workload can be taken over bi the remaining nodes in the cluster. This paper presents how to provide the guaranteeing high availability of clustering web server. The load balancer becomes a single failure point of the whole system. In order to prevent the failure of the load balancer, we setup a backup server using heartbeat, fake, mon, and checkpointing fault-tolerance method. For high availability of file servers in the cluster, we setup coda file system. Coda is a advanced network fault-tolerance distributed file system.

• Journal of KIISE:Software and Applications
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• v.26 no.1
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• pp.130-130
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• 1999

In this paper, we propose a novel design method for improving performance of existing FCM-type clustering algorithms. First, we define the performance measure which focuses on bothcompactness and separation of clusters. Next, we optimize this measure using evolution program.Especially the proposed method has following merits: ① using evolution program, it solves suchproblems as initialization, number of clusters, and convergence to local optimum ② it reduces searchspace and improves convergence speed of algorithm since it represents chromosome with possiblepotential centers which are selected possible candidates of centers by density measure ③ it improvesperformance of clustering algorithm with the performance index which embedded both compactnessand separation Properties ④ it is robust to noise data since it minimizes its effect on center search.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.95-96
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• 2018

Edge Computing은 Cloud Computing의 단점을 보완하기 위해 등장 하였으나, 자원 제한을 가지고 있는 Edge Node에서 데이터 분석 및 처리해야 하는 문제점이 있다. 이를 해결하기 위해 K-means clustering 알고리즘과 PCA 기법을 이용해 차원 추축을 이용한 계산비용과 처리시간을 줄이는 기법을 제안하였다. PCA란, 차원 축소 및 데이터 압축에 사용되는 기계학습 알고리즘 중 하나이며, 데이터에서 중요한 정보만 추출해 차원을 줄일 수 있다. 이를 통해 제안한 기법이 기존의 Reduction first clustering second(RFCS) 기법에 비해 성능이 우수한 것을 확인할 수 있었다.

• Journal of information and communication convergence engineering
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• v.16 no.4
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• pp.258-263
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• 2018

Color Doppler sonography is a useful tool for examining blood flow and related indices. However, it should be done by well-trained operator, that is, operator subjectivity exists. In this paper, we propose an automatic blood flow area extraction method from brachial artery that would be an essential building block of computer aided color Doppler analyzer. Specifically, our concern is to examine hypertension suspicious (prehypertension) patients who might develop their symptoms to established hypertension in the future. The proposed method uses fuzzy C-means clustering as quantization engine with careful seeding of the number of clusters from histogram analysis. The experiment verifies that the proposed method is feasible in that the successful extraction rates are 96% (successful in 48 out of 50 test cases) and demonstrated better performance than K-means based method in specificity and sensitivity analysis but the proposed method should be further refined as the retrospective analysis pointed out.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.65-77
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• 2021

COVID-19 has recently impacted the world with the large numbers of infected and deaths. The development of effective COVID-19 vaccine has not been successful. Hence, people have a high concern on the infection of this disease. The infection information from the governmantal public organizations are mainly based on simple summary statistics. Consequently, it is hard to assess the infection risks of individual person and the current location of the person. In this paper, we present a machine learning-based software system that analyzes COVID-19 infection risks and guidelines for safe activities.This paper proposes a suite of risk factors regarding COVID-19 infection and deaths and methods to quantitatively measure the individual and group risks using the proposed metrics. The proposed system utilizes a clustering algorithms and various software approaches that reflect the information and features of inviduals and their geograpical locations.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.516-524
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• 2004

In this paper, the initial value problem in clustering using K-means or Fuzzy-c-means is considered to reduce the number of iterations. Conventionally the initial values in clustering using K-means or Fuzzy-c-means are chosen randomly, which sometimes brings the results that the process of clustering converges to undesired center points. The choice of intial value has been one of the well-known subjects to be solved. The system of clustering using K-means or Fuzzy-c-means is sensitive to the choice of intial values. As an approach to the problem, the uniform partitioning method is employed to extract the optimal initial point for each clustering of data. Experimental results are presented to demonstrate the superiority of the proposed method, which reduces the number of iterations for the central points of clustering groups.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.668-670
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• 2014

데이터가 폭발적으로 증가함에 따라 필요한 정보들을 찾는 것은 더욱더 어려워지고 개인의 생각이나 많은 자료들을 SNS 공간을 통해 공유함으로써 프라이버시 유출도 많아지게 된다. 대부분의 SNS는 자신의 공간에 게재된 정보에 대한 접근권한 만을 설정할 수 있고 자신이 타인의 공간에 게재한 게시물에 대해서는 접근 권한 설정에 대한 자격을 부여하지 않는다. 이를 통해 원치 않은 사용자들에게 까지 자신의 개인 정보가 노출되는데 얼마든지 개인 정보의 유출로 인한 문제들이 일어날 수 있다. 따라서 본 논문에서는 서비스 제공자가 제 3자에게 SNS 그래프 데이터 제공시 개인 정보의 노출을 차단하기 위해 K-Means Clustering 기법을 사용한 방법을 보인다.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1443-1450
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• 2004

The clustering characteristics for a number of classes, and defining the inheritance relations between the classes is a difficult and complex problem in an early stage of object oriented software development. We discuss a traditional iterative approach for the reuse of the existing classes in a library and an integrated approach to creating a number of new classes presented in this study. This paper formulates a character-istic clustering problem for zero-one integer programming and presents a network solution method with illustrative examples and the basic rules to define the inheritance relations between the classes. The network solution method for a characteristic clustering problem is based on a distance parameter between every pair of objects with characteristics. We apply the approach to a real problem taken from industry.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1249-1257
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• 2017

Due to the 4th industrial revolution and an aged society, many studies are being conducted to apply virtual reality to medical field. Research on dementia is especially active. This paper proposes virtual reality based on cognitive rehabilitation contents using image recognition and clustering method to improve cognitive and physical disabilities caused by dementia. Unlike the existing cognitive rehabilitation system, this paper uses travel photos that reflect the memories of the subjects to be treated. In order to generate automated cognitive rehabilitation contents, we extract face information, food pictures, place information, and time information from photographs, and normalization is performed for clustering. And we present scenarios that can be used as cognitive rehabilitation contents using travel photos in virtual reality space.