• 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.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.1005-1013
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• 2021

With the prolonged COVID-19, the existing academic gap is widening. The purpose of this study is to provide homeroom teachers with a visual confirmation of the academic achievement gap in grades and classrooms through academic achievement analysis, and to use this to help them design lessons and explore ways to improve the academic achievement gap. The data of students' Korean and math diagnostic evaluation scores at the beginning of the school year were visualized as clusters using the K-means algorithm, and as a result, it was confirmed that a meaningful clusters were formed. In addition, through the results of the teacher interview, it was confirmed that this system was meaningful in improving the academic achievement gap, such as checking the learning level and academic achievement of students, and designing classes such as individual supplementary instruction and level-specific learning. This means that this academic achievement data analysis system helps to improve the academic gap. This study provides practical help to homeroom teachers in exploring ways to improve the academic gap in grades and classes, and is expected to ultimately contribute to improving the academic gap.

• Journal of Engineering Education Research
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• v.8 no.1
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• pp.31-45
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• 2005

In this paper, selecting survey items was performed using three clustering methods: factor analysis, fuzzy c-Means algorithm and cluster analysis. The methods were used to extract key items from various questionnaires. The key item represents several similar questionnaires that form a cluster. Test survey was made of 120 items obtained from several surveys and it was answered by 646 students from 4 universities. Each item contains 6 choices. Applying the clustering method chose 25 items which is reduced from the original 120 items. The results yielded by three methods are very similar.

• Land and Housing Review
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• v.14 no.2
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• pp.35-50
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• 2023

The purpose of this study is to identify areas in need of urban renewal by utilizing spatial data and analyzing their types and characteristics. For this, this research employed a kernel density function and K-means cluster analysis with spatial data, through which it sought ways to identify high-demand areas for urban renewal projects. The key findings and implications of the research are summarized as follows. Firstly, this research classified 587 target sites in Seoul based on development density (ratios) and an indicator for aged buildings. Approximately half of these areas were consistent with leading pilot project sites and Accelerated Integration Sites. Secondly, it was observed that residential environments in the designated leading pilot project sites, as decided by public sectors, were relatively poor compared to other areas. Lastly, the target areas for urban renewal were not clearly categorized through statistical analysis. Instead, it was found that categorization should be made depending on the requirements of each project.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.3
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• pp.73-83
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• 2003

Methods of fuzzy C-means have been used to characterize geotechnical information from static cone penetration data. As contrary with traditional classification methods such as Robertson classification chart, the FCM expresses classes not conclusiveness but fuzzy. The results show that the FCM is useful to characterize ground information that can not be easily found by using normal classification chart. But optimal number of classes may not be easily defined. So, the optimal number of classes should be determined considering not only technical measures but engineering aspects.

• Journal of Korea Technology Innovation Society
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• v.20 no.1
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• pp.81-102
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• 2017

Financing, manpower support and tax are the most popular tools for policy for small and medium enterprises (SMEs). This paper, however, will introduce information analysis support for SMEs and will prove that can be a good tool. The information analysis support means the support of technology and market information for the technology development or commercialization of SMEs. Therefore, the support is a customized one. In the theory domain, we adopt and prove two theoretical grounds as an SMEs policy such as market and system failure. In the policy tool domain, we suggest four requirements to be an SMEs policy and prove the tool to satisfy these requirements. All the data and proofs are from a government research institute called K.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.142.2-142.2
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• 2015

Plasma etch endpoint detection (EPD) of SiO2 and PR layer is demonstrated by plasma impedance monitoring in this work. Plasma etching process is the core process for making fine pattern devices in semiconductor fabrication, and the etching endpoint detection is one of the essential FDC (Fault Detection and Classification) for yield management and mass production. In general, Optical emission spectrocopy (OES) has been used to detect endpoint because OES can be a simple, non-invasive and real-time plasma monitoring tool. In OES, the trend of a few sensitive wavelengths is traced. However, in case of small-open area etch endpoint detection (ex. contact etch), it is at the boundary of the detection limit because of weak signal intensities of reaction reactants and products. Furthemore, the various materials covering the wafer such as photoresist (PR), dielectric materials, and metals make the analysis of OES signals complicated. In this study, full spectra of optical emission signals were collected and the data were analyzed by a data-mining approach, modified K-means cluster analysis. The K-means cluster analysis is modified suitably to analyze a thousand of wavelength variables from OES. This technique can improve the sensitivity of EPD for small area oxide layer etching processes: about 1.0 % oxide area. This technique is expected to be applied to various plasma monitoring applications including fault detections as well as EPD.

• Journal of the Korean Society of Clothing and Textiles
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• v.10 no.3
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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.

• Journal of the Korean Society of Tobacco Science
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• v.13 no.2
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• pp.59-65
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• 1991

There was no a difference of genetic analysis among methods(means, joint scaling test, 3 Parameter model) . The magnitude of additive effects generally paralleled the magnitude of difference between parental means and appeared to be more independent from non-allelic interaction than did dominance effects, whereas the magnitude of dominance effects were inflated by non-allelic interaction. Additive effects were significant for all characteristics observed and it was a major effects in inheritance of number of leaves. Dominance effects were higher than additive effects for plant height, days to flower, flesh leaf weight per plant, curing rate, total alkaloid and total nitrogen.

• Proceedings of the Safety Management and Science Conference
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• 2009.11a
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• pp.565-572
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• 2009

This paper presents the optimization steps with weight and importance of estimated characteristic values in the multiresponse surface analysis(MRA). The research introduces the shape parameter of individual desirability function for relaxation and tighening of specification bounds. The study also proposes the combinded desirability function using arithmetic, geometric and harmonic means considering the measurement unit and numerical pattern.