• The Korean Journal of Community Living Science
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• v.24 no.4
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• pp.553-565
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• 2013

The purpose of this study is to segment urban farming patterns and analyse the preference on urban farming by applying two-step cluster analysis, chi-square analysis and ANOVA. The data was collected by face-to-face surveys on 9 urban farms which were chosen by location and operator. Respondents were asked to describe their urban farming pattern, preference on urban farming and socio-demographic characteristics. Three different clusters were derived from two-step cluster analysis based on operator, transportation, number of visits per month, travel time, area and motive. They were 'hobby-oriented farming', 'producing-oriented farming', and 'multipurpose farming'. The results of chi-square analysis showed that there were significant differences among these three segmented groups in terms of age, with or without children aged under 14, household income per month and housing type. In other words, there is quite a possibility that urbanites have different urban farming patterns according to their socio-demographic profiles. Also, there were significant differences on the preference on urban farming by cluster. According to the results, planning directions of urban farming were presented.

• 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.

• Communications for Statistical Applications and Methods
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• v.21 no.3
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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.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.43-46
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• 2008

Centrality has been actively studied in network analysis field. In this paper we show a calculation method of closeness centrality for WSN. Since nodes in a sensor network are very scarce in energy and computation capability the calculation of the closeness is done in two tiers by dividing network into clusters. In first step closeness centrality for cluster heads is calculated. In the second step closeness of member nodes of the chosen cluster is computed in respect to that cluster itself.

• The Journal of Information Systems
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• v.26 no.4
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• pp.227-246
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• 2017

Purpose: As the importance of software has been emphasized recently, the size of the software market is continuously expanding. The development of the software market is being adversely affected by software piracy. In this study, we try to classify countries around the world based on the macro environmental factors, which influence software piracy. We also try to identify the differences in software piracy for each classified type. Design/methodology/approach: The data-driven approach is used in this study. From the BSA, the World Bank, and the OECD, we collect data from 1990 to 2015 for 127 environmental variables of 225 countries. Cronbach's ${\alpha}$ analysis, item-to-total correlation analysis, and exploratory factor analysis derive 15 constructs from the data. We apply two-step approach to cluster analysis. The number of clusters is determined to be 5 by hierarchical cluster analysis at the first step, and the countries are classified by the K-means clustering at the second step. We conduct ANOVA and MANOVA in order to verify the differences of the environmental factors and software piracy among derived clusters. Findings: The five clusters are identified as underdeveloped countries, developing countries, developed countries, world powers, and developing country with large market. There are statistically significant differences in the environmental factors among the clusters. In addition, there are statistically significant differences in software piracy rate, pirated value, and legal software sales among the clusters.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2016.11a
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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.

• 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.

• Journal of Information Technology Applications and Management
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• v.26 no.1
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• pp.53-64
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• 2019

The purpose of this study is to find out the fast delivery route that several drones return a truck again after departing from it for delivery locations at each cluster while the truck goes through the cluster composed of several delivery locations. The main issue is to reduce the total delivery time composed of the delivery time by relatively slow trucks via clusters and the sum of maximum delivery times by relatively fast drones in each cluster. To solve this problem, we use a three-step heuristic approach. First, we cluster the nearby delivery locations with minimal number of clusters satisfying a constraint of drone flight distance to set delivery paths for drones in each cluster. Second, we set an optimal delivery route for a truck through centers of the clusters using the TSP model. Finally, we find out the moved centers of clusters while maintaining the delivery paths for the truck and drones and satisfying the constraint of drone flight. distance in the two-dimensional region to reduce the total delivery time. In order to analyze the effect of this study model according to the change of the number of delivery locations, we developed a R-based simulation prototype and compared the relative efficiency, and performed paired t-test between TSP model and the cluster-based models. This study showed its excellence through this experimentation.

• 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.

• Korean Journal of Social Welfare Studies
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• v.47 no.4
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• pp.333-360
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• 2016

Social Impact Bonds(SIBs) has emerged as a promising way to finance proven social services programs, fund what works, and drive government accountability and has increased fast. In this context, this study examines and classifies the structure of SIB focusing key criteria of the main steps through two-step cluster analysis. Analysis results are as follows. First, the main commissioners of SIB are the ministries of the central government. And in terms of the stage of invest, there are usually two or three investors mainly consisting of social finance organizations. Second, in terms of target and age of SIB beneficiaries, it focuses on the adolescent and youth. Third, in the outcome evaluation stage, the results show that in most cases outcome payments are determined by a validation of service provider or government administrative data. However, the rate of payments are based on the comparison of the program beneficiaries to other comparable groups is 23.8%. Finally, The results of two-step cluster analysis are as; 'mix of central government and social finance organization', 'multiple agent including private organization', and 'multiple social provider'. Among three types, 'multiple agent including private organization' shows the most active participation between agents and the most systematic outcome measurement and management. The results of this study imply that the importance of the method to manage and measure outcome in SIB structure. Moreover, the consist of commissioners or investors is needed to expanded more.