• International Journal of Costume and Fashion
• /
• v.17 no.1
• /
• pp.41-57
• /
• 2017

The purpose of this research was to develop a typology of fair-trade apparel consumers and present a clear overview of the influence of product knowledge on consumer behaviors. A two-step cluster analysis was used to classify respondents into sub-groups based on their level of self-perceived knowledge and purchase experience. In addition, ANOVA was used to test the predictive validity of the cluster solution. Income was the only demographic variable that significantly differed across groups. The more familiar and more experienced group had higher income than the other groups. Psychographic data showed that attitudes and moral norms varied across groups. The more familiar and more experienced group had more positive attitudes and higher level of moral norms than the less familiar and less experienced group. In terms of behavioristic data, groups differed significantly in purchase intentions and willingness to pay more for fair-trade apparel. That is, the more familiar and more experienced group was willing to pay more and had higher purchase intentions than the less familiar and less experienced group. Overall, the level of product knowledge and purchase experience were considerably low. This study thus confirmed the need to increase consumers' familiarity related to fair-trade apparel products.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.11a
• /
• pp.121-124
• /
• 2000

We introduce an advanced mountain clustering method which uses a normalized data space, a gaussian type mountain function and a deconstruction method using mountain slope. This is more useful than Yagers mountain method because it needs just one parameter to tune instead of three and finds out more resonable cluster centers. Computational examples are presented to show the validity of the advanced mountain method.

• Journal of Gifted/Talented Education
• /
• v.24 no.4
• /
• pp.509-541
• /
• 2014

There was a great need to consider a core competency-based approach as a new direction of the science education for gifted students according to the value and vision of the 21st century knowledge-based societies. Therefore we developed a core competency model of scientist and examined its validity as a prerequisite for a core competency-based education of science gifted students. In order to this, the survey was conducted after developing questionnaire through the theoretical review of the various resources such as paper, book, and newspaper articles and the qualitative analysis of the behavioral event interview, and then an exploratory factor analysis was performed to validate the factor structure based on the results of the survey. The results revealed that the core competency model with the 5 cluster units of competency and the 15 core competencies was potentially constituted. And the reliability, convergent validity, and discriminant validity of the core competency model were verified through the confirmatory factor analysis. The cognitive cluster consisted of 5 competencies and they were as follows: creative, comprehensive, exploratory, analytical, and conceptual thinking competency. The achievement-orientation cluster consisted of 3 competencies and they were as follows: initiative, preparation & problem solving, and strategic influence competency. The scientific attitude cluster consisted of 3 competencies and they were as follows: flexible thinking & attitude, passion for research, and views about science competency. The personal effectiveness cluster consisted of 2 competencies and they were as follows: diverse experiences and global attitude competency. Finally, the networking cluster consisted of 2 competencies and they were as follows: personal understanding and communication competency. Findings were expected to provide the basic data for developing programs and establishing strategies based on the core competency as well as introducing the core competency model of scientist to science education for gifted students effectively.

• Journal of Korea Technology Innovation Society
• /
• v.15 no.3
• /
• pp.675-699
• /
• 2012

Recently innovation cluster has been emphasized as a valid policy measure for innovation. For suggesting a new model of innovation cluster, we conducted literature review for the theories of regional innovation system(RIS) and innovation clusters. As a result, the success factors of innovation cluster were classified into system measures, physical measures, and social(human resources) measures from the perspective of RIS. The case of Gyeonggi province's innovation cluster policy was examined for an empirical analysis. For an in-depth analysis, a survey for the tenant companies in Pangyo technovalley(PTV) was conducted. As a result, it was found that Gyeonggi province's innovation cluster policy is evolving from 'a government-initiated system' to 'the 3rd-generation convergence RIS' and that PTV is closely related to the system measures of 'support for up-to-date management and technology information' and 'support for academic-research-industry cooperative research and technological cooperation,' physical measures of 'strengthening the function of PTV support division' and 'shared research equipment and other infrastructures,' and social measures of 'legal, financial, and administrative support system' and 'public childcare facilities'. Also, technology sector was divided into IT and non-IT for a validity check to find out that there was no significant difference in all factors, except for the public relations factors. This study is expected to provide resources for other related researches.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.26 no.6
• /
• pp.51-65
• /
• 2023

The Jeongmaeks are Korea's unique forest space recognition system that diverged from the Baekdudaegan. The Jeongmaeks are easily exposed to pressure because it is adjacent to the living area. Among them, Nakdong-Jeongmaek has high biodiversity, but damage is accelerating. According to the Convention on Biological Diversity (CBD) in 2022, the target is to expand the area of terrestrial and marine protected areas to 30% of national territory by 2030. As of September 2023, the area of terrestrial protected areas in South Korea is only 16.97% of the country's territory. This is due in part to the high proportion of private forests in the region, which makes it difficult to establish protected areas. Therefore, there is a need to establish Other Effective Area-based Conservation Measure (OECMs), which pursue complex and effective conservation that considers multiple values, as an alternative to protected areas. This study aims to identify areas suitable for OECM and to provide opinions on the establishment of appropriate management plans for each value using SOM and InVEST Habitat Quality model. This study evaluated the habitat quality of 206 villages located within 1km of the Nakdong-Jeongmaek and compared the characteristics of villages classified by SOM. As a result, the habitat quality was 0.867 for Tourism village (ClusterIV), 0.838 for Conservation village (ClusterVI), 0.835 for Mixed village (ClusterI), 0.796 for Production (ClusterV), 0.731 for Rural village (ClusterIII) and 0.625 for Urban village (ClusterII). When the distribution was identified through statistical analysis, the Kruskal-Wallis test showed that the distributions were not identical, with a p-value of 1.53e-08. Dunn's test showed a difference between Tourism, Conservation and Rural, Urban village. However, Mixed village was overestimated due to the lack of villages and the small area included in the study area. Moreover, Conservation village was somewhat under-evaluated in the analysis due to the use of a single weight for protected areas. It is necessary to perform additional reinforcement of the value evaluation of Jeongmaeks by conducting Forest Resource Survey and the National Natural Environment Survey. Therefore, we believe that sufficient validity for the establishment of OECMs in the Nakdong-Jeongmaek can be provided by addressing these limitations and conducting additional research.

• The Korean Journal of Applied Statistics
• /
• v.24 no.6
• /
• pp.1213-1224
• /
• 2011

In high dimensionality where the number of variables are excessively larger than observations, it is required to remove the noninformative variables to cluster observations. Most model-based approaches for variable selection have been considered under the assumption of homoscedasticity and their models are mainly estimated by a penalized likelihood method. In this paper, a different approach is proposed to remove the noninformative variables effectively and to cluster based on the modified normal mixture model simultaneously. The validity of the model was provided and an EM algorithm was derived to estimate the parameters. Simulation studies and an experiment using real microarray dataset showed the effectiveness of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.5B
• /
• pp.557-565
• /
• 2011

In this paper, we assume that sensor nodes organize the multi-hop networks, are fixed, and operate as full function devices(FFD). The wireless sensor network(WSN) only consists of mobile nodes without the assistance from the fixed infrastructure, which increases the flexibility of the network. However, it is difficult to perform routing in the WSN, since sensor nodes freely join in and drop out of the network, and some sensor nodes have very low power. We propose the algorithm combining routing schemes based on the bitmap and cluster methods in this paper. Through computer simulations, we show the validity of the proposed algorithm.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.9
• /
• pp.3029-3036
• /
• 2000

Image segmentation is one of the important processes in the image information extraction for computer vision systems. The fuzzy clustering methods have been extensively used in the image segmentation because it extracts feature information of the region. Most of fuzzy clustering methods have used the Fuzzy C-means(FCM) algorithm. This algorithm can be misclassified about the different size of cluster because the degree of membership depends on highly the distance between data and the centroids of the clusters. This paper proposes a fuzzy clustering algorithm using the Average Intracluster Distance that classifies data uniformly without regard to the size of data sets. The Average Intracluster Distance takes an average of the vector set belong to each cluster and increases in exact proportion to its size and density. The experimental results demonstrate that the proposed approach has the g

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.4 no.1
• /
• pp.251-259
• /
• 2000

As a server dependency for the availability and scalability becomes very important, the need for solid server providing non-stop workload have been increased. So, this paper describes a server configuration method for the availability and scalability. For the validity check of this paper, socket application and cluster resource DLL and administration DLL for the application are implemented and tested. 8y relocating the individual failed services from one sewer to another with the microsoft cluster sewer, it was confirmed the feasibility. The result showed that allowed applications on the original server to continue running, unaffected by the failed service.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.2
• /
• pp.823-827
• /
• 2016

Determination of the colorectal cancer stage is possible only after surgery based on pathology results. However, sometimes this may prove impossible. The aim of the present study was to determine colorectal cancer stage using three clustering methods based on preoperative clinical findings. All patients referred to the Colorectal Research Center of Shiraz University of Medical Sciences for colorectal cancer surgery during 2006 to 2014 were enrolled in the study. Accordingly, 117 cases participated. Three clustering algorithms were utilized including k-means, hierarchical and fuzzy c-means clustering methods. External validity measures such as sensitivity, specificity and accuracy were used for evaluation of the methods. The results revealed maximum accuracy and sensitivity values for the hierarchical and a maximum specificity value for the fuzzy c-means clustering methods. Furthermore, according to the internal validity measures for the present data set, the optimal number of clusters was two (silhouette coefficient) and the fuzzy c-means algorithm was more appropriate than the k-means clustering approach by increasing the number of clusters.