• 제목/요약/키워드: Hierarchical cluster analysis

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제조 셀 구현을 위한 군집분석 기반 방법론 (Cluster Analysis-based Approach for Manufacturing Cell Formation)

  • 심영학;황정윤
    • 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.

컴포넌트 분류를 위한 복합 클러스터 분석 방법 (A Composite Cluster Analysis Approach for Component Classification)

  • 이성구
    • 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.

Hierarchical Cluster Analysis Histogram Thresholding with Local Minima

  • Sengee, Nyamlkhagva;Radnaabazar, Chinzorig;Batsuuri, Suvdaa;Tsedendamba, Khurel-Ochir;Telue, Berekjan
    • 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".

Symbolic Cluster Analysis for Distribution Valued Dissimilarity

  • Matsui, Yusuke;Minami, Hiroyuki;Misuta, Masahiro
    • Communications for Statistical Applications and Methods
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    • 제21권3호
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    • pp.225-234
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    • 2014
  • We propose a novel hierarchical clustering for distribution valued dissimilarities. Analysis of large and complex data has attracted significant interest. Symbolic Data Analysis (SDA) was proposed by Diday in 1980's, which provides a new framework for statistical analysis. In SDA, we analyze an object with internal variation, including an interval, a histogram and a distribution, called a symbolic object. In the study, we focus on a cluster analysis for distribution valued dissimilarities, one of the symbolic objects. A hierarchical clustering has two steps in general: find out step and update step. In the find out step, we find the nearest pair of clusters. We extend it for distribution valued dissimilarities, introducing a measure on their order relations. In the update step, dissimilarities between clusters are redefined by mixture of distributions with a mixing ratio. We show an actual example of the proposed method and a simulation study.

HRKT: A Hierarchical Route Key Tree based Group Key Management for Wireless Sensor Networks

  • Jiang, Rong;Luo, Jun;Wang, Xiaoping
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제7권8호
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    • pp.2042-2060
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    • 2013
  • In wireless sensor networks (WSNs), energy efficiency is one of the most essential design considerations, since sensor nodes are resource constrained. Group communication can reduce WSNs communication overhead by sending a message to multiple nodes in one packet. In this paper, in order to simultaneously resolve the transmission security and scalability in WSNs group communications, we propose a hierarchical cluster-based secure and scalable group key management scheme, called HRKT, based on logic key tree and route key tree structure. The HRKT scheme divides the group key into cluster head key and cluster key. The cluster head generates a route key tree according to the route topology of the cluster. This hierarchical key structure facilitates local secure communications taking advantage of the fact that the nodes at a contiguous place usually communicate with each other more frequently. In HRKT scheme, the key updates are confined in a cluster, so the cost of the key updates is reduced efficiently, especially in the case of massive membership changes. The security analysis shows that the HRKT scheme meets the requirements of group communication. In addition, performance simulation results also demonstrate its efficiency in terms of low storage and flexibility when membership changes massively.

군집분석 기법을 이용한 공공도서관 그룹화에 대한 연구 (A Study of Library Grouping using Cluster Analysis Methods)

  • 곽철완
    • 한국비블리아학회지
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    • 제31권3호
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    • pp.79-99
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    • 2020
  • 이 연구의 목적은 공공도서관 그룹화를 위해 적합한 군집분석 모델을 파악하고 그 특징을 분석하는데 있다. 국가도서관통계시스템의 공공도서관 통계 데이터를 사용하였으며, 군집분석 기법의 3가지 모델을 적용하였다. 공공도서관 규모를 기준으로 군집분석을 실시한 결과 크게 2가지 군집으로 구분되었으며, 군집의 크기는 크게 한쪽으로 치우쳤다. 그룹화 모델로 도서관 규모를 기준으로 삼으면, 계층적 군집분석의 와드측정법과 k-평균군집분석 모델이 적합하였다. 공공도서관 그룹화 연구 결과에 대한 시사점은 다음과 같다. 첫째, 통계 데이터 외에 도서관 서비스 관련 다양한 데이터 수집이 진행되어야 한다. 둘째, 분석 대상이 되는 데이터 세트에 적합한 분석 모델이 적용되어야 한다. 셋째, 도서관 서비스 향상을 위해 군집분석 기법의 다양한 분야 적용 가능성에 대한 적극적인 연구가 필요가 있다.

Hierarchical Cluster Analysis on Competitiveness of Container Terminals in Northern Vietnam

  • Nguyen, Minh-Duc;Kim, Sung-June;Jeong, Jung-Sik
    • 한국항해항만학회지
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    • 제40권2호
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    • pp.67-72
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    • 2016
  • Vietnam's sea-port industry has experienced a significant development in recent years. Especially in Northern Vietnam, both the demand and supply of handling services for containerized cargoes have increased at a considerable rates. Accompany with such movement, the competition among container terminals in the area becomes fiercer. In this paper, Hierarchical Cluster Analysis is employed to classify all 11 container terminals in Northern Vietnam by collecting data concerning terminal competitiveness. After the classification, each group will be discussed in order to reveal more details about their competitive characteristics. The paper consists of five sections. Section 1 is the general introduction. Section 2 provides a general literature review about competitiveness and factors to evaluate competitiveness. Section 3 explains variables and methodology applied to do the analysis. Section 4 presents the results with linkage to the current condition. Section 5 summarizes the analysis results. It is shown that container terminals in Northern Vietnam should not only pay attention to their service qualities but also have to find out an appropriate mechanism to avoid unhealthy competition. The paper is expected to contribute a background for further researches in container terminals' competition in the region as well as hints for operators in planning and making decisions.

A Study of HME Model in Time-Course Microarray Data

  • Myoung, Sung-Min;Kim, Dong-Geon;Jo, Jin-Nam
    • 응용통계연구
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    • 제25권3호
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    • pp.415-422
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    • 2012
  • For statistical microarray data analysis, clustering analysis is a useful exploratory technique and offers the promise of simultaneously studying the variation of many genes. However, most of the proposed clustering methods are not rigorously solved for a time-course microarray data cluster and for a fitting time covariate; therefore, a statistical method is needed to form a cluster and represent a linear trend of each cluster for each gene. In this research, we developed a modified hierarchical mixture of an experts model to suggest clustering data and characterize each cluster using a linear mixed effect model. The feasibility of the proposed method is illustrated by an application to the human fibroblast data suggested by Iyer et al. (1999).

Toxoplasma gondii virulence prediction using hierarchical cluster analysis based on coding sequences (CDS) of sag1, gra7 and rop18

  • Subekti, Didik T;Ekawasti, Fitrine;Desem, Muhammad Ibrahim;Azmi, Zul
    • Journal of Veterinary Science
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    • 제22권6호
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    • pp.88.1-88.6
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    • 2021
  • Toxoplasma gondii consists of three genotypes, namely genotype I, II and III. Based on its virulence, T. gondii can be divided into virulent and avirulent strains. This study intends to evaluate an alternative method for predicting T. gondii virulence using hierarchical cluster analysis based on complete coding sequences (CDS) of sag1, gra7 and rop18 genes. Dendrogram was constructed using UPGMA with a Kimura 80 nucleotide distance measurement. The results showed that the prediction errors of T. gondii virulence using sag1, gra7 and rop18 were 7.41%, 6.89% and 9.1%, respectively. Analysis based on CDS of gra7 and rop18 was able to differentiate avirulent strains into genotypes II and III, whereas sag1 failed to differentiate.

피복구성학적 인체계측과 집낙구조분석 ( I ) (Anthropometry for clothing construction and cluster analysis ( I ))

  • 김구자
    • 한국의류학회지
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    • 제10권3호
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    • pp.37-48
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    • 1986
  • The purpose of this study was to analyze 'the natural groupings' of subjects in order to classify highly similar somatotype for clothing construction. The sample for the study was drawn randomly out of senior high school boys in Seoul urban area. The sample size was 425 boys between age 16 and 18. Cluster analysis was more concerned with finding the hierarchical structure of subjects by three dimensional distance of stature. bust girth and sleeve length. The groups forming a partition can be subdivided into 5 and 6 sets by the hierarchical tree of the given subjects. Ward's Minimum Variance Method was applied after extraction of distance matrix by the Standardized Euclidean Distance. All of the above data was analyzed by the computer installed at Korea Advanced Institute of Science and Technology. The major findings, take for instance, of 16 age group can be summarized as follows. The results of cluster analysis of this study: 1. Cluster 1 (32 persons means $18.29\%$ of the total) is characterized with smaller bust girth than that of cluster 5, but stature and sleeve length of the cluster 1 are the largest group. 2. Cluster 2 (18 Persons means $10.29\%$ of the total) is characterized with the group of the smallest stature and sleeve length, but bust girth larger than that of cluster 3. 3. Cluster 3(35persons means $20\%$ of the total) is classified with the smallest group of all the stature, bust girth and sleeve length. 4. Cluster 4(60 persons means $34.29\%$ of the total) is grouped with the same value of sleeve length with the mean value of 16 age group, but the stature and bust girth is smaller than the mean value of this age group. 5. Cluster 5(30 persons means $17.14\%$ of the total) is characterized with smaller stature than that of cluster 1, and with larger bust girth than that of cluster 1, but with the same value of the sleeve length with the mean value of the 16 age group.

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