• Communications for Statistical Applications and Methods
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• 제22권1호
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• pp.55-67
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• 2015

An important problem in cluster analysis is the selection of variables that define cluster structure that also eliminate noisy variables that mask cluster structure; in addition, outlier detection is a fundamental task for cluster analysis. Here we provide an automated K-means clustering process combined with variable selection and outlier identification. The Automated K-means clustering procedure consists of three processes: (i) automatically calculating the cluster number and initial cluster center whenever a new variable is added, (ii) identifying outliers for each cluster depending on used variables, (iii) selecting variables defining cluster structure in a forward manner. To select variables, we applied VS-KM (variable-selection heuristic for K-means clustering) procedure (Brusco and Cradit, 2001). To identify outliers, we used a hybrid approach combining a clustering based approach and distance based approach. Simulation results indicate that the proposed automated K-means clustering procedure is effective to select variables and identify outliers. The implemented R program can be obtained at http://www.knou.ac.kr/~sskim/SVOKmeans.r.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2001년도 하계 학술대회 논문집(KISPS SUMMER CONFERENCE 2001
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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.

• Communications for Statistical Applications and Methods
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• 제9권2호
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• pp.505-514
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• 2002

Multiple correspondence analysis graphically shows the correspondent relationship among categories in multi-way contingency tables. It is well known that the proportions of the principal inertias as part of the total inertia is low in multiple correspondence analysis. Moreover, although this problem can be overcome by using the Benzecri formula, it is not enough to show clear correspondent relationship among categories (Greenacre and Blasius, 1994, Chapter 10). In addition, they show that Andrews' plot is useful in providing the correspondent relationship among categories. However, this method also does not give some concise interpretation among categories when the number of categories is large. Therefore, in this study, we will easily interpret the multiple correspondence analysis by applying the K-means clustering analysis.

• 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.

• Communications for Statistical Applications and Methods
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• 제19권2호
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• pp.213-217
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• 2012

Fuzzy k-means clustering is an attractive alternative to the ordinary k-means clustering in analyzing multivariate data. Fuzzy versions yield more natural output by allowing overlapped k groups. In this study, we modify a fuzzy k-means clustering algorithm to be used for undirected social networks, apply the algorithm to both real and simulated cases, and report the results.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2003년도 추계학술대회
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• pp.249-258
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• 2003

K-means clustering has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research and so on. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters that we want, because it is more primitive, explorative. In this paper we propose a new method of k-means clustering using the grid-based sample. It is more fast than any traditional clustering method and maintains its accuracy.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2003년도 추계학술대회
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• pp.229-238
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• 2003

K-means clustering has been widely used in many applications, such that pattern analysis, data analysis, market research and so on. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters, because it is more primitive and explorative. In this paper we propose a new method of k-means clustering using the grid-based representative value(arithmetic and trimmed mean) for sample. It is more fast than any traditional clustering method and maintains its accuracy.

• 정보보호학회논문지
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• 제31권1호
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• pp.1-9
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• 2021

본 논문은 RSA 복호화 과정에서 발생한 전력 신호로부터 암호연산을 예측하는 과정을 주파수 분석과 K-means 알고리즘을 이용하여 자동화하는 것을 제안한다. RSA 복호화 과정은 제곱 연산과 곱셈 연산으로 나뉘며, 시간에 따른 연산의 종류를 예측하게 되면, RSA 암호의 키(key)값을 알 수 있게 된다. 본 논문은 복호화 과정에서 발생한 전력 파형을 2차원 주파수 신호로 변환한 후, K-means algorithm을 이용하여 연산의 종류에 따라 주파수 벡터를 분류하였다. 이후, 이러한 분류된 주파수 벡터를 이용하여 연산의 종류를 예측한다.

• Journal of the Korean Data and Information Science Society
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• 제15권1호
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• pp.83-94
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• 2004

In this study we analyze categorical data by means of spatial statistics and echelon analysis. To do this, we first determine the hierarchical structure of a given contingency table by using echelon dendrogram then, we detect candidates of hotspots given as the top echelon in the dendrogram. Next, we evaluate spatial scan statistics for the zones of significantly high or low rates based on the likelihood ratio. Finally, we detect hotspots of any size and shape based on spatial scan statistics.

• 한국융합학회논문지
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• 제10권1호
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• pp.291-298
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• 2019

본 연구의 목적은 잠재평균분석을 활용하여 여성장애인의 자녀양육역량, 양육스트레스, 회복탄력성, 사회적 지지 수준이 장애유형(정신장애, 신체장애)에 따라 차이가 있는지 탐색하는 것이다. 이를 위해 여성장애인의 장애유형에 따라 정신장애 167명, 신체장애 132명으로 구분하고 설문조사를 실시하였다. 연구 결과, 양육스트레스와 사회적 지지 수준은 정신장애 여성에서 더 높게, 자녀양육역량과 회복탄력성은 신체장애 여성에서 더 높게 나타났다. 이러한 결과를 바탕으로 장애유형에 따라 차별화된 실천적 정책적 개입전략을 제시하였다.