• Title, Summary, Keyword: 2 step cluster analysis

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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.

Segmentation and Characteristic Analysis of Urban Farmers Behavior (도시농업 활동 유형화 연구)

  • Hwang, Jeong-Im;Choi, Yoon-Ji;Jang, Bo-Gyung;Rhee, Sang-Young
    • The Korean Journal of Community Living Science
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    • v.21 no.4
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    • pp.619-631
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    • 2010
  • The purpose of this study is to segment and examine urban farmers behavior by applying a two-step cluster analysis and multi-nominal logit model. The data were collected by a telephone survey with two-staged stratified random sampling in the cities around the country for the purpose of acquiring representative data. Respondents were asked to describe their awareness of urban agriculture, their agricultural activity, and sociodemographic characteristics. Among 2,000 cases, 381 cases(19.1%) which were of participants in urban agriculture were analysed in SPSS. From the findings, 27.3% of respondents had heard the word 'urban agriculture', and 25.5% of them regarded themselves as urban farmers. Four different clusters were derived from two-step clusters based on motive, place, companion, area and hours. They were 'Large scale hobby farming(cluster 1)', ‘Weekend farm/ hobby farming(cluster 2)', 'Land/ Self-supporting farming(cluster 3)', and 'Small scale hobby farming(cluster 4)'. The result of multinomial logistic regression showed that there were significant differences among these four segmented groups in terms of age, city size and housing type. In other words, there is quite a possibility that urbanites select different urban farming types according to their socio-demographic profiles. Therefore, the urbanite profiles can be used as the basis for promoting policy of several urban agriculture types. According to the result, policy directions for facilitating urban agriculture were presented.

Assessing the Differences in Korean View on National Economic Policy with Factor and Cluster Analysis

  • Kim, Hee-Jae;Yun, Young-Jun
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.2
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    • pp.451-461
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    • 2008
  • In this study, factor and cluster analysis have been conducted to group the differences in Korean view on national economic policy in the sample of the 2006 Korean General Social Survey (KGSS). According to the 2006 KGSS, the 6 items with a 5-point Likert scale include the questions about whether or the extent to which each respondent supports the specific types of governmental economic policy. In our study, at first, the factor analysis has converted the original 6 items into the 3 composite variables that account for 81% in the total variability. As the second step of factor analysis, factor scores have been computed. Then, the K-means cluster analysis based on the factor scores has been conducted to group the survey respondents into the 3 clusters. In particular, the cross-tabulation analysis has shown that the distribution of the 3 clusters varies with the respondents' socio-demographic characteristics.

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Transformation of PEO coatings from crater to cluster-based structure with increase in DC voltage and the role of ZrO2nanoparticles

  • Rehman, Zeeshan Ur;Shin, Seong Hun;Koo, Bon Heun
    • Proceedings of the Korean Institute of Surface Engineering Conference
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    • pp.111-111
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    • 2016
  • Two step PEO ceramic coatings were formed on AZ91 magnesium alloy in $ZrO_2$ nanoparticles and $K_2ZrF_6$ based colloidal electrolyte solution for various voltages. Surface and layers tructure of the coatings was analyzed using SEM (ScanningElectronMicroscope). Structure analysis revealed that surface of the coating was transferred from individual pancake or craters-based structure to cluster-based structure with increasing the voltage of the secondary step process. Further, it was confirmed that the cluster zone was richin Zr-based complexes and formed due to high intensives parks. Increase in the Zr contents as discovered from the EDS analysis confirmed the rise in amorphous form of the Zr-based species, which justified the results of XRD where no increase in the intensity of Zr-based species was observed with increase in voltage. Potentiodynamic polarizariotion and impedance spectroscopy techniques were used to evaluate the corrosion performance of the coatings. The highest corrosion resistance was found for coatings prepared at 240V. The same specimen was found having highest and uniform vickers hardness ~1070.5 HV. The superior mechanical and electrochemical properties of the said coating can be attributed to the defect-less microstructure and the optimal role of $ZrO_2$ nanoparticles in the secondary PEO process at 240V.

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MR Brain Image Segmentation Using Clustering Technique

  • Yoon, Ock-Kyung;Kim, Dong-Whee;Kim, Hyun-Soon;Park, Kil-Houm
    • Proceedings of the IEEK Conference
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    • pp.450-453
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    • 2000
  • In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 steps. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional (3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with it’s initial centroid value as the outstanding cluster’s centroid value. The proposed segmentation algorithm complements the defect of FCM algorithm, being influenced upon initial centroid, by calculating cluster’s centroid accurately And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the results of single spectral analysis.

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Evaluation of Shopping Items: Focused on Purchase of Foreign Tourists in South Korea

  • Jeong, Dong-Bin
    • East Asian Journal of Business Economics (EAJBE)
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    • v.7 no.2
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    • pp.21-30
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    • 2019
  • Purpose - In this work, we categorize the 21 shopping items which foreign tourists purchase in South Korea and monitor the level of dissimilarity (or similarity) between each item by utilizing distance matrix, and both hierarchical and k-means cluster analyses, respectively, based on several purpose of visit attributes in 2017. In addition, multidimensional scaling (MDS) method is applied for mining visual appearance of proximities among shopping items based on purpose of visit attributes. Research design and methodology - This study is carried out in 2017 by Ministry of Culture, Sports and Tourism and conduct a face-to-face survey of foreign tourists from 20 countries who purchase shopping items in South Korea. CLUSTER, PROXIMITIES and ALSCAL modules in IBM SPSS 23.0 are used to perform this work. Results - We ascertain that 21 shopping items can be classified into five similar groups which have homogeneous traits by going through two-step cluster analysis. We can position homogeneous places of cluster and shopping items joining each cluster. Conclusions - We can relatively assess patterns and characteristics of each shopping item, come by useful information in activating shopping tour based on the actual state of recognition of foreign tourists and practically apply to each tourism industry on underlying results.

The similarities analysis of location fishing information through 2 step clustering (2단계 군집분석을 통한 해구별 조업정보의 유사성 분석)

  • Cho, Yong-Jun
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.3
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    • pp.551-562
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    • 2009
  • In this paper, I would present a using method for The Fishing Operation Information(FOI) of National Federation of Fisheries Cooperatives(NFFC) through the availabilities analysis and put out the similarities by the section of the sea through classifying characteristics of fishing patterns by their locations. As a result, although the catch of FOI is nothing more than 33% level to National Fishery Production Statistics(NFPS), FOI data is useful in understanding the patterns of fishing operation by the location because both patterns and correlation were very similar in the usability analysis, comparing the FOI data with NFPS. So I classified optimal clusters for catch, the number of fishing days and the number of fishing vessels through 2 step cluster analysis by the big marine zone and divided fishing patterns.

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Approximate Fuzzy Clustering Based on Density Functions (밀도함수를 이용한 근사적 퍼지 클러스처링)

  • 권석호;손세호
    • Journal of the Korean Institute of Intelligent Systems
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    • v.10 no.4
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    • pp.285-292
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    • 2000
  • In general, exploratory data analysis consists of three processes: i) assessment of clustering tendency, ii) cluster analysis, and iii) cluster validation. This analysis method requiring a number of iterations of step ii) and iii) to converge is computationally inefficient. In this paper, we propose a density function-based approximate fuzzy clustering method with a hierachical structure which consosts of two phases: Phase I is a features(i.e., number of clusters and cluster centers) extraction process based on the tendency assessment of a given data and Phase II is a standard FCM with the cluster centers intialized by the results of the Phase I. Numerical examples are presented to show the validity of the proposed clustering method.

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A BAYESIAN VIEW ON FARADAY ROTATION MAPS - SEEING THE MAGNETIC POWER SPECTRUM IN CLUSTERS OF GALAXIES

  • VOGT CORINA;ENBLIN TORSTEN A.
    • Journal of The Korean Astronomical Society
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    • v.37 no.5
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    • pp.349-353
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    • 2004
  • Magnetic fields are an important ingredient of galaxy clusters and are indirectly observed on cluster scales as radio haloes and radio relics. One promising method to shed light on the properties of cluster wide magnetic fields is the analysis of Faraday rotation maps of extended extragalactic radio sources. We developed a Fourier analysis for such Faraday rotation maps in order to determine the magnetic power spectra of cluster fields. In an advanced step, here we apply a Bayesian maximum likelihood method to the RM map of the north lobe of Hydra A on the basis of our Fourier analysis and derive the power spectrum of the cluster magnetic field. For Hydra A, we measure a spectral index of -5/3 over at least one order of magnitude implying Kolmogorov type turbulence. We find a dominant scale of about 3 kpc on which the magnetic power is concentrated, since the magnetic autocorrelation length is ${\lambda}_B = 3 {\pm} 0.5\;kpc$. Furthermore, we investigate the influences of the assumption about the sampling volume (described by a window function) on the magnetic power spectrum. The central magnetic field strength was determined to be ${\~}7{\pm}2{\mu}G$ for the most likely geometries.

WEAK GRAVITATIONAL LENSING ANALYSIS OF A SAMPLE OF 50 MASSIVE GALAXY CLUSTERS

  • PHRIKSEE, A.;COVONE, G.;KOMONJINDA, S.;SERENO, M.
    • Publications of The Korean Astronomical Society
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    • v.30 no.2
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    • pp.393-395
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    • 2015
  • Weak gravitational lensing is an efficient technique for detecting galaxy clusters and probing their mass distribution. We present a weak gravitational lensing analysis of a large sample of galaxy clusters. We have built a nearly complete sample of 50 optically rich clusters, located in the redshift range 0.1 < z < 0.6 and observed in the Canada France Hawaii Telescope Legacy Survey (CFHT-LS). We used weak gravitational lensing to measure, for each galaxy cluster, the density radial profile, the total mass and the mass-to-light ratio (by comparing with the total luminosity of the member galaxies). This project is a preliminary step towards the next analysis of the weak lensing galaxy clusters in the surveys KiDS and VOICE, which are currently collecting data with the VLT Survey Telescope, in Chile.