• 산업경영시스템학회지
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• 제36권1호
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• pp.24-35
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• 2013

A cell formation approach based on cluster analysis is developed for the configuration of manufacturing cells. Cell formation, which is to group machines and parts into machine cells and the associated part families, is implemented to add the flexibility and efficiency to manufacturing systems. In order to develop an efficient clustering procedure, this paper proposes a cluster analysis-based approach developed by incorporating and modifying two cluster analysis methods, a hierarchical clustering and a non-hierarchical clustering method. The objective of the proposed approach is to minimize intercellular movements and maximize the machine utilization within clusters. The proposed approach is tested on the cell formation problems and is compared with other well-known methodologies available in the literature. The result shows that the proposed approach is efficient enough to yield a good quality solution no matter what the difficulty of data sets is, ill or well-structured.

• 정보처리학회논문지D
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• 제14D권1호
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• pp.89-96
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• 2007

컴포넌트 재사용을 위해 다양한 분류 방법들이 개발되어 왔다. 이러한 분류 방법들은 사용자가 필요로 하는 컴포넌트들을 쉽고 빠르게 접근하는 것을 돕는다. 전통적인 분류 방법들은 분류 구조 생성을 위한 도메인 분석 노력, 컴포넌트 사이의 관계 표현, 도메인 진화에 따른 분류 구조 유지 보수의 어려움, 그리고 한정된 도메인 적용과 같은 문제들을 포함한다. 본 논문은 이러한 문제들을 언급하기 위해 복합 클러스터 분석 기반의 컴포넌트 분류 방법에 대해 묘사한다. 안정적인 분류 구조 자동 생성을 위해 계층 클러스터 분석 방법과 새로운 컴포넌트의 자동 분류에 대해 비계층 클러스터 분석 개념은 결합된다. 제안된 방법에 의해 생성된 클러스터 정보는 관련 컴포넌트들에 대한 도메인 분석 과정을 지원할 수 있다.

• 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.

• The Journal of Asian Finance, Economics and Business
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• 제3권2호
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• pp.51-57
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• 2016

The aim of this study is to examine of the cluster approach to ensure high rates of innovation, information and communication enterprises of information technology cluster in order to enhance the competitiveness of regions. Keeping with the previous literature, the present research determined that the novelty of the problem, concerning of the creation IT clusters as drivers of new generation, i.e. a kind of platform of "startup accelerators" through the creation of previously not existing in the country high-tech industries and sectors of the economy. The study employs system approach involves to determine prospective directions of the formation of clusters of IT industry, also applies structured approach to shows relationships between elements of cluster systems (participants of cluster), as well as focusing on some aspects of cluster development such as networking. Based on this analysis we have proposed to create clusters in regions, which can play the role of translator's innovations at the periphery of the country. This research shows that formation of IT clusters is one of the most successful tools to avoid of dependence of Kazakhstan from raw materials.

• Journal of the Korean Data Analysis Society
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• 제20권6호
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• pp.3049-3067
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• 2018

최근 조직몰입(organizational commitment) 3요인(정서적 몰입, 규범적 몰입, 지속적 몰입) 모델(Allen, Meyer, 1990, 1991) 관련 연구에서 사람중심 접근법(person-centered approach)이 많은 관심을 받고 있지만 아직 국내 연구자들에게는 크게 확산되지 못하고 있다. 이에 본 논문은 사람중심 접근법의 개념과 관련 자료분석 방법을 군집분석(cluster analysis)과 잠재프로파일분석(latent profile analysis)을 중심으로 소개하고자 한다. 또한 이러한 방법들의 실제 적용 사례를 제시하기 위해 국내 직장인들 349명으로부터 수집한 자료를 바탕으로 군집분석과 잠재프로파일분석을 각각 실행하여 각 분석으로부터 6개의 조직몰입 프로파일 유형들을 도출하였으며 그 결과를 비교하였다. 뿐만 아니라, 조직몰입 프로파일 유형 간 이직의도(turnover intention)의 차이를 살펴봄으로써 도출된 프로파일의 타당성을 확인하였다. 본 논문은 조직몰입에 대한 기존의 변수중심 접근법(variable-centered approach)에 대한 보완적 방법으로 사람중심 접근법이 갖는 의미를 이해하고, 나아가 국내 직장인들의 조직몰입 프로파일 유형을 살펴볼 수 있다는 점에서 국내 연구자들 뿐만 아니라 실무담당자들에게 시사하는 바가 크다고 할 수 있다.

• Journal of Multimedia Information System
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• 제4권4호
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• pp.189-194
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• 2017

In this study, we propose a method which is based on "Image segmentation by histogram thresholding using hierarchical cluster analysis"/HCA/ and "A nonparametric approach for histogram segmentation"/NHS/. HCA method uses that all histogram bins are one cluster then it reduces cluster numbers by using distance metric. Because this method has too many clusters, it is more computation. In order to eliminate disadvantages of "HCA" method, we used "NHS" method. NHS method finds all local minima of histogram. To reduce cluster number, we use NHS method which is fast. In our approach, we combine those two methods to eliminate disadvantages of Arifin method. The proposed method is not only less computational than "HCA" method because combined method has few clusters but also it uses local minima of histogram which is computed by "NHS".

• Smart Structures and Systems
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• 제26권1호
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• 한국항만학회지
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• 제13권1호
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• pp.11-26
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• 1999

The subject of port efficiency is one of the important issues facing port authorities and policy makers today. A number of studies have been undertaken which compare ports in terms of their efficiency. But any port comparison can only be valid and meaningful if a port’s efficiency is compared with a similar port. The main objective of this paper is to introduce a systematic approach to identifying similar ports based on the technique of principal component analysis and cluster analysis. And it seeks to identify the most important factors underlying the port classification. Lack of awareness of which factors differentiate ports has resulted in an unnecessary collection of data which are of limited use in port classification. This paper has identified five groupings of similar ports within which port comparision can be justifiably made. This approach can be used for any future port comparision.

• 한국경영과학회지
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• 제28권4호
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• pp.39-60
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• 2003

In this study, we evaluate financial performance of 21 domestic life insurers using SAW (simple additive weighting), ELECTREII, cluster analysis respectively, and suggest a hybrid approach of combining cluster analysis and ELECTREII to reclassify the life insurers into more meaningful groups according to their respective financial features. We also perform the sensitivity analysis employing ANOVA and Tukey's test to examine the robustness of ELECTREII, which would be influenced by decision maker's subjective preference parameters. Consequently, it is shown that ELECTREII turns out to be a flexible method providing decision makers with useful ranking Information especially under fuzzy decision making situation with incomparable alternatives, and hence it can serve as a complementary method to overcome the weakness of classical cluster analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권4호
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• pp.1237-1255
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• 2014

In wireless sensor networks (WSNs), hierarchical clustering is an efficient approach for lower energy consumption and extended network lifetime. In cluster-based multi-hop communications, a cluster head (CH) closer to the sink is loaded heavier than those CHs farther away from the sink. In order to balance the energy consumption among CHs, we development a novel cluster-based routing protocol for corona-structured wireless sensor networks. Based on the relaying traffic of each CH conveys, adequate radius for each corona can be determined through nearly balanced energy depletion analysis, which leads to balanced energy consumption among CHs. Simulation results demonstrate that our clustering approach effectively improves the network lifetime, residual energy and reduces the number of CH rotations in comparison with the MLCRA protocols.