• Title, Summary, Keyword: 군집분석

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A Comparative Study on Statistical Clustering Methods and Kohonen Self-Organizing Maps for Highway Characteristic Classification of National Highway (일반국도 도로특성분류를 위한 통계적 군집분석과 Kohonen Self-Organizing Maps의 비교연구)

  • Cho, Jun Han;Kim, Seong Ho
    • Journal of The Korean Society of Civil Engineers
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    • v.29 no.3D
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    • pp.347-356
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    • 2009
  • This paper is described clustering analysis of traffic characteristics-based highway classification in order to deviate from methodologies of existing highway functional classification. This research focuses on comparing the clustering techniques performance based on the total within-group errors and deriving the optimal number of cluster. This research analyzed statistical clustering method (Hierarchical Ward's minimum-variance method, Nonhierarchical K-means method) and Kohonen self-organizing maps clustering method for highway characteristic classification. The outcomes of cluster techniques compared for the number of samples and traffic characteristics from subsets derived by the optimal number of cluster. As a comprehensive result, the k-means method is superior result to other methods less than 12. For a cluster of more than 20, Kohonen self-organizing maps is the best result in the cluster method. The main contribution of this research is expected to use important the basic road attribution information that produced the highway characteristic classification.

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인위적 데이터를 이용한 군집분석 프로그램간의 비교에 대한 연구 - A Research-In-Progress Paper -

  • 김성호;백승익;최종연
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • pp.349-357
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    • 2000
  • 인터넷 비즈니스나 전자상거래와 연관되어 고객관계관리 (Customer Relationship Management: CRM)가 널리 확산됨으로 해서 군집분석에 대한 관심이 한층 높아졌고, 다양한 군집분석 프로그램이 시장에 소개되어 지고 있다. 그러나, 군집분석 프로그램들은 다른 데이터 분석 기법과는 달리 그들의 정확성을 측정하기가 매우 힘들다. 본 논문에서는 이미 알려져 있는 군집구조를 지닌 인위적 데이터를 사용하여 반복적 군집분석 프로그램 (Convergent Cluster Analysis: CCA)과 보다 전통적인 단순군집 프로그램 (One-Shot Clustering Program: Howard-Harris 프로그램), 그리고 데이터 마이닝 기법 중의 하나인 데모그래픽 군집분실 프로그램의 정확성을 비교하기 위한 현재 진행 중인 연구의 방법론을 제시하는데 그 주요 목적을 두고 있다.

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A Comparison of Cluster Analyses and Clustering of Sensory Data on Hanwoo Bulls (군집분석 비교 및 한우 관능평가데이터 군집화)

  • Kim, Jae-Hee;Ko, Yoon-Sil
    • The Korean Journal of Applied Statistics
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    • v.22 no.4
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    • pp.745-758
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    • 2009
  • Cluster analysis is the automated search for groups of related observations in a data set. To group the observations into clusters many techniques has been proposed, and a variety measures aimed at validating the results of a cluster analysis have been suggested. In this paper, we compare complete linkage, Ward's method, K-means and model-based clustering and compute validity measures such as connectivity, Dunn Index and silhouette with simulated data from multivariate distributions. We also select a clustering algorithm and determine the number of clusters of Korean consumers based on Korean consumers' palatability scores for Hanwoo bull in BBQ cooking method.

The Analysis of the Forest Community Structure of Mt. Minjuji (민주지산의 산림군집구조분석)

  • 최송현;조현서;이경재
    • Korean Journal of Environment and Ecology
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    • v.11 no.1
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    • pp.111-125
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    • 1997
  • To investigate the climax forest structure and to construct the ecological basic data, forty nine plots were set up and surveyed in Mt. Minjuji, Chungchongpukdo. According to the analysis of classification by TWINSPAN, the community was divided by seven groups of Pinus densiflora-Carpinus laxiflora-Quercus serrata(community I), Q. mongolica-Q. serrata-Platycarya strobilacea(community II), Q. mongolica(community III), Fraxinus mandshurica-Acer mono(community IV), Cornus controversa-F. mandshurica(community V), F. mandshurica-Carpinus cordata(community VI), and F. mandshurica-C. laxiflora(community VII). In the results of the analysis of species structure, similarity, diversity and DBH, except for community I~III, it was founede out broadleaves-mixed-climax forest. Constructed basic data will be applied to sustainable development such as ecotourism, nature trail etc.

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An Enhanced Multidimensional Scaling Technique Combined with Clustering Results for Knowledge Domain Analysis (지적 구조 분석을 위한 군집분석과 다차원척도법의 결합 방안)

  • Lee, Jae Yun
    • Proceedings of the Korean Society for Information Management Conference
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    • pp.3-6
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    • 2010
  • 연구동향 분석이나 연구영역 분석에서 널리 사용되고 있는 다차원척도법은 표현할 개체의 수가 많을 경우에 군집분석 결과와 잘 결합되지 못하는 단점이 있다. 이를 해결하기 위해서 군집분석과 다차원척도법을 결합하는 새로운 방법을 제안하고 실제 사례에 적용해보았다.

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A Study of the Fuzzy Clustering Algorithm using a Growth Curve Model (성장곡선을 이용한 퍼지군집분석 기법의 연구)

  • 김응환;이석훈
    • The Korean Journal of Applied Statistics
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    • v.14 no.2
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    • pp.439-448
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    • 2001
  • 본 연구는 시간자료(Longitudinal data)의 분석을 위하여 Fuzzy k-means 군집분석 방법을 확장한 알고리즘을 제안한다. 이 논문에서 제안하는 군집분석방법은 각각의 개체에 대응하는 성장곡선에 Fuzzy k-means 군집분석의 알고리즘을 결합하는 것을 핵심아이디어로한다. 분석결과는 생성된 군집을 성장곡선모형으로 표현할 수 있고 또한 추정된 모형의 식을 활용하여 새로운 개체를 분류도 할수 있음을 보인다. 그리고 이 군집분석방법은 아직 자라지 않은 나이 어린 개체가 미래에 어느 군집에 속할 것인가 하는 분류와 함께 이 개체의 향후 성장상태를 예측을 하는 데에도 적용이 가능하다. 제안된 알고리즘을 원숭이(macaque)의 상악동(maxillary sinus)의 자료에 적용한 실례로 보인다.

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Bayesian analysis of finite mixture model with cluster-specific random effects (군집 특정 변량효과를 포함한 유한 혼합 모형의 베이지안 분석)

  • Lee, Hyejin;Kyung, Minjung
    • The Korean Journal of Applied Statistics
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    • v.30 no.1
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    • pp.57-68
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    • 2017
  • Clustering algorithms attempt to find a partition of a finite set of objects in to a potentially predetermined number of nonempty subsets. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet prior distribution calculates posterior probabilities when the number of clusters was known. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. Examples are given to show how these models perform on real data.

A Strategy Through Segmentation Using Factor and Cluster Analysis: focusing on corporations having a special status (요인분석과 군집분석을 통한 세분화 및 전략방향 제시: 특수법인 사례를 중심으로)

  • Cho, Yong-Jun;Kim, Yeong-Hwa
    • The Korean Journal of Applied Statistics
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    • v.20 no.1
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    • pp.23-38
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    • 2007
  • Corporations adopt a segmentation depends on the existence of target variables, in general. In this paper, for the case of no target variables, a strategy through segmentation is proposed for corporations having a special status based on the management index. In case of segmentation using cluster analysis, however, if one classify according to many variables then he will be in face of difficulties in characterizing. Therefore, after extracting representative factors by factor analysis, a segmentation method through 2 step cluster analysis is employed on the basis of these representative factors. As a result, six segmentation groups are found and the resulting strategy is proposed which strengthens prominent factors and makes up defective factors for each group.

Plant Community Structure Analysis in Jujeongol Valley of Soraksan National Park (설악산 국립공원 주전골계곡 식물군집구조분석)

  • 이경재;민성환;한봉호
    • Korean Journal of Environment and Ecology
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    • v.10 no.2
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    • pp.283-296
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    • 1997
  • To investigate the plant community structure in valley and suggest the management of Mational Park, fifty plots were set up and surveyed in Jujeongol Valley, Soraksan National Park. The classification by TWINSPAN and DCA ordination technique were applied to the study area in order to classify them into several groups based on woody plants. The dividing groups were Quercus mpnngolica - Q. variabilis - Pinus densiflora community, P. densiflora community, Carpinus laxiflora community, Q. serrata community. The ecological trends of tree species by DCA ordination technique and DBH class distribution analysis was like that Q. mongolica - Q. variabilis - P. densiflora community and P. densiflora community seems to be trended from P. densiflora community to Q. mongolica community. Q. serrata community seems to be trended from Q. serrata community to C. laxiflora community and C. laxiflora will be maintaimed stable state.

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Cluster analysis by month for meteorological stations using a gridded data of numerical model with temperatures and precipitation (기온과 강수량의 수치모델 격자자료를 이용한 기상관측지점의 월별 군집화)

  • Kim, Hee-Kyung;Kim, Kwang-Sub;Lee, Jae-Won;Lee, Yung-Seop
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.5
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    • pp.1133-1144
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    • 2017
  • Cluster analysis with meteorological data allows to segment meteorological region based on meteorological characteristics. By the way, meteorological observed data are not adequate for cluster analysis because meteorological stations which observe the data are located not uniformly. Therefore the clustering of meteorological observed data cannot reflect the climate characteristic of South Korea properly. The clustering of $5km{\times}5km$ gridded data derived from a numerical model, on the other hand, reflect it evenly. In this study, we analyzed long-term grid data for temperatures and precipitation using cluster analysis. Due to the monthly difference of climate characteristics, clustering was performed by month. As the result of K-Means cluster analysis is so sensitive to initial values, we used initial values with Ward method which is hierarchical cluster analysis method. Based on clustering of gridded data, cluster of meteorological stations were determined. As a result, clustering of meteorological stations in South Korea has been made spatio-temporal segmentation.