• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.913-921
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• 2005

We describe tests for detecting and locating quantitative traits loci (QTL) for traits in Hanwoo. Lod scores and a permutation test have been described. From results of a permutation test to detect QTL, we select major DNA markers of BMS941 microsatellite locus in Hanwoo chromosome 17 for further analysis. K-means clustering analysis applied to four traits and eight DNA markers in BMS941 resulted in three cluster groups. We conclude that the major DNA markers of BMS941 microsatellite locus in Hanwoo chromosome 17 are markers 80bp, 85bp 90bp and 105bp.

• 디지털산업정보학회논문지
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• 제17권1호
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• pp.75-84
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• 2021

For analyzing big data, the social network is increasingly being utilized through relational data, which means the connection characteristics between entities such as people and objects. When the relational data does not exist directly, a social network can be configured by calculating relational data such as attribute similarity from attribute data of entities and using it as links. In this paper, the composition method of the social network using the attribute similarity between entities as a connection relationship, and the clustering method using subgroups for the configured social network are suggested, and the clustering effectiveness of the clustering results is evaluated. The analysis results can vary depending on the type and characteristics of the data to be analyzed, the type of attribute similarity selected, and the criterion value. In addition, the clustering effectiveness may not be consistent depending on the its evaluation method. Therefore, selections and experiments are necessary for better analysis results. Since the analysis results may be different depending on the type and characteristics of the analysis target, options for clustering, etc., there is a limitation. In addition, for performance evaluation of clustering, a study is needed to compare the method of this paper with the conventional method such as k-means.

• Communications for Statistical Applications and Methods
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• 제11권3호
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• pp.657-668
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• 2004

Permutation test has been applied for the QTL(quantitative trait loci) analysis and we selected a major locus. K -means clustering analysis, for the major DNA Marker mining of ILSTS035 microsatellite loci in Hanwoo chromosome 6, has been described. Finally, bootstrap testing method has been adapted to calculate confidence intervals and for finding major DNA Markers.

• 응용통계연구
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• 제23권2호
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• pp.367-374
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• 2010

As an alternative to the k-means clustering the k-spatial medians clustering has many good points because of advantages of spatial median. However, it has not been used a lot since it needs heavy computation. If the number of objects and the number of variables are large the computation time problem is getting serious. In this study we propose fast algorithm for the k-spatial medians clustering. Practical applicability of the algorithm is shown with some numerical studies.

• Journal of Advanced Marine Engineering and Technology
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• 제40권8호
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• pp.726-732
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• 2016

Clustering analysis is widely used in data mining to classify data into categories on the basis of their similarity. Through the decades, many clustering techniques have been developed, including hierarchical and non-hierarchical algorithms. In gene profiling problems, because of the large number of genes and the complexity of biological networks, dimensionality reduction techniques are critical exploratory tools for clustering analysis of gene expression data. Recently, clustering analysis of applying dimensionality reduction techniques was also proposed. PCA (principal component analysis) is a popular methd of dimensionality reduction techniques for clustering problems. However, previous studies analyzed the performance of PCA for only full data sets. In this paper, to specifically and robustly evaluate the performance of PCA for clustering analysis, we exploit an improved FCBF (fast correlation-based filter) of feature selection methods for supervised clustering data sets, and employ two well-known clustering algorithms: k-means and k-medoids. Computational results from supervised data sets show that the performance of PCA is very poor for large-scale features.

• 지식경영연구
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• 제10권4호
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• pp.17-30
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• 2009

This study is concerned with suggesting a new clustering algorithm to evaluate the value of papers which were supported by research grants by Korea Research Fund (KRF). The algorithm is based on an extended version of a conventional PSO (Particle Swarm Optimization) mechanism. In other words, the proposed algorithm is based on integration of k-means algorithm and simulated annealing mechanism, named KASA-PSO. To evaluate the robustness of KASA-PSO, its clustering results are evaluated by research grants experts working at KRF. Empirical results revealed that the proposed KASA-PSO clustering method shows improved results than conventional clustering method.

• 한국전기전자재료학회논문지
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• 제20권10호
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• pp.901-906
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• 2007

In this study, we measured the partial discharge phenomenon of inside the closed switchgear, using ultra wide band antenna. The characteristics of $\Phi-q-n$ in the normal state are stable, and confirmed at less than 0.01, but in proceeding states, about 2 times larger. And in the abnormal state, it grew hundreds of times larger compared with normal state. According to K-means analysis, if slant of discharge characteristics is a straight line close to "0" and standard deviation is small, it is in a normal state. However if we can find a peak from K-means clusters and standard deviation to be large, it is in an abnormal state.

• 전기학회논문지
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• 제65권9호
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• pp.1557-1563
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• 2016

According to recent studies, poor sitting posture of the spine has been shown to lead to a variety of spinal disorders. For this reason, it is important to measure the sitting posture. We proposed a strategy for classification of sitting posture using machine learning. We retrieved acceleration data from single tri-axial accelerometer attached on the back of the subject's neck in 5-types of sitting posture. 6 subjects without any spinal disorder were participated in this experiment. Acceleration data were transformed to the feature vectors of principle component analysis. Support vector machine (SVM) and K-means clustering were used to classify sitting posture with the transformed feature vectors. To evaluate performance, we calculated the correct rate for each classification strategy. Although the correct rate of SVM in sitting back arch was lower than that of K-means clustering by 2.0%, SVM's correct rate was higher by 1.3%, 5.2%, 16.6%, 7.1% in a normal posture, sitting front arch, sitting cross-legged, sitting leaning right, respectively. In conclusion, the overall correction rates were 94.5% and 88.84% in SVM and K-means clustering respectively, which means that SVM have more advantage than K-means method for classification of sitting posture.

• 한국정보통신학회논문지
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• 제18권3호
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• pp.625-630
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• 2014

컴퓨터의 발전과 인터넷의 급속한 발전으로 정보의 양이 폭발적으로 증가하게 되었고 이러한 방대한 양의 정보들은 대부분 문서 형태로 관리되기 때문에, 이들을 효과적으로 검색하고 처리하는 방법의 연구가 필요하다. 문서 군집은 문서간의 유사도를 바탕으로 서로 연관된 문서들을 군집화하여 대용량의 문서들을 자동으로 분류하고 검색하고 처리하는데 효율과 정확성을 증대시킨다. 본 논문은 특징 벡터 공간 상의 벡터들로 표현되는 문서들을 K 평균 알고리즘으로 군집화할 때, 주성분 분석을 사용하여 초기 시드점들을 선정함으로써 군집의 효율을 높이는 방법을 제안한다. 실험 결과를 통하여 제안하는 기법이 기존의 K 평균 알고리즘보다 좋은 결과를 얻을 수 있음을 보였다.

• Composites Research
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• 제37권1호
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• pp.46-52
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• 2024

운송 수단의 발전은 인간의 교통 편의 증진과 더불어 이동이 불편한 장애인들의 이동 반경 확대를 가능하게 하였다. 그러나 휠체어 탑재 차량의 경우 차량 사고 시 발생할 수 있는 안전성은 일반 승객 좌석에 비해 여전히 낮다. 특히 무방비 상태에서 발생할 수 있는 후방 추돌 사고의 경우 장애인 탑승객의 목 부상에 치명적으로 작용할 수 있다. 따라서 휠체어 탑재 차량에 적용될 headrest에는 보다 세밀한 설계안이 반영되어야 한다. 본 연구에서는 휠체어 운송 차량의 저속 후방 추돌 시 headrest의 국부적 압축 특성 분포 구현을 위해 multi-cell headrest가 제안되었다. 이후 해석을 통한 데이터셋 구축과 k-means clustering을 적용한 군집화 결과를 이용해 탑승객의 목 상해지수와 충격 에너지 흡수량 간 상관관계 분석이 수행되었다. 군집화 결과 유사한 특성을 지닌 데이터 군집이 형성된 것을 확인하였으며, 각 군집의 특성을 통한 목 상해지수와 충격 에너지 흡수량 간의 상관관계 분석이 수행되었다. 분석 결과 Mid3와 Mid6에서의 cell 압축 특성이 soft할수록 충격 에너지 흡수량이 증가하는 것을 확인하였으며, Front2, Mid3, Mid6에서의 cell 압축 특성이 hard할수록 목 상해지수 감소에 효과적임을 확인하였다.