• The Journal of Industrial Distribution & Business
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• v.9 no.12
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• pp.15-21
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• 2018

Purpose - In order to improve the comprehensive competitiveness of cultural tourism creative industries in the region, different grade cities should adapt to local conditions and differentiated development as the angle. It can promote the optimization of regional tourism industry structure, and make relevant suggestions accordingly. Research design, data, and methodology - Based on the characteristics of cultural tourism creative industry, 24 indicators are selected from four aspects. The comprehensive competitiveness evaluation index system of cultural tourism creative industry is constructed. The essay use factor analysis to quantitatively evaluate 27 cities in the Pan-Yangtze River Delta, and use cluster analysis to classify the competitiveness of cultural tourism creative industries in each city into five types. Results - The results of cluster analysis show that the whole area presents five kinds of step-like features, which are mature leading type, the advantageous development type, comparatively advantageous type, well-grounded type and difference-promotion type. Conclusions - The factor analysis method was used to quantitatively evaluate 27 cities in the Pan-Yangtze River Delta, and cluster analysis was used to classify the cultural tourism creative industry competitiveness of each city into fives type city. Based on the differentiation of cultural tourism creative industry competitiveness and 27 cities in the Pan-Yangtze River Delta, the relevant strategies were proposed.

• The Journal of Fisheries Business Administration
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• v.40 no.3
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• pp.127-146
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• 2009

This study is to estimate the factor weights of the reasons for decreases in marine fishery resources using the Analytical Hierarchy Process. Furthermore, it classifies 20 fishes under a fishery resource recovery plan into various groups of fishes according to these factor weights using the non-hierarchial cluster analysis. The factors of decreases in marine fishery resources are identified as bio-ecological, technology-system, economic-business, and fishing village-society factors. Two of the most important factors of decreases in resource are turned out to be the economic-business and bio-ecological factors, estimated as 31% and 30% respectively. The technology-system and fishing village-society factors are estimated as 21% and 18% respectively. The study utilizes non-hierarchical cluster analysis in order to classify 20 fishes into 2, 3, and 4 groups. K-means cluster analysis is applied for grouping in conjunction with ANOVA to identify statistical differences in factors. Once again, the economic-business and bio-economic factors play main role in grouping 2-groups of fishes case. The third group of fishes in addition to the previous 2 groups of fishes appears as those 4 factors of decrease evenly play about the same role at a 3-groups of fishes case. Finally, the economic-business and bio-economic factors are turned out to be evenly important in the 4th group once there are 4-groups of fishes.

• 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.

• 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.

• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.126-131
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• 2020

This study analyzed the inflow and outflow patterns of emergency department patients, to identify changes in regional types in cities, counties, and districts in Jeonlado, Korea. Data of areas in Jeonlado for 2014 to 2018 were extracted from the National Emergency Department Information System. The extracted data includes the patients' and emergency medical institution addresses, which were used to calculate the relevance index (RI) and commitment index (CI). The calculated indices were classified into regional types by applying cluster analysis. A non-parametric method, Kruskal-Wallis test, was employed to examine the differences between years for RI and CI by regional types. The results of cluster analysis using the relevance and commitment indices revealed three regional types. Regions in cluster 1 were classified as outflow type, in cluster 2 as inflow type, and in cluster 3 as self-sufficient. RI and CI were calculated for each cluster or regional type. There were no significant differences between years in cluster 2 (inflow type) and cluster 3 (self-sufficient type). In cluster 1 (outflow type), there were no significant differences in CI between the years; however, there were significant differences in RI between 2014 and 2017, and 2014 and 2018. It is difficult to see that the emergency medical environment has improved due to the increased concentration of emergency medical care.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.773-787
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• 2003

Principal component analysis, factor analysis and cluster analysis have been performed to analyze the relationship between air pollutants and meteorological variables measured in 1999 in Seoul. In principal analysis, the first principal has been shown the contrast effect between $O_3$ and the other pollutants, the second principal has been shown the contrast effect between CO, $SO_2$, $NO_2$ and $O_3$, PM10, TSP. In factor analysis, the first factor has been found as PM10, TSP, $NO_2$ concentrations which are related with suspended particulates. As a result of cluster analysis, three clusters respectively have represented different air pollution levels, seasonal characteristics of air pollutants and meteorological situations.

• Journal of Korean Clinical Nursing Research
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• v.23 no.1
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• pp.20-29
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• 2017

Purpose: The purpose of this study was to investigate symptoms experienced by patients who reported fever at the emergency room (ER) and to identify any existing cluster of symptom related to fever. Methods: The study used a retrospective and descriptive design with secondary data analysis. Data were abstracted from 665 medical records of patients with fever who visited ER from September 1, 2015 to August 31, 2016 at a tertiary hospital. Results: The most frequently reported symptoms included cold (43.9%), myalgia (24.1%), headache (16.2%), general weakness (15.3%), respiratory symptoms (12.3%), gastrointestinal (GI) symptoms (12.0%), mental change (4.5%), sweating(1.8%), and warmth (0.9%). Analysis of the symptoms related to fever revealed seven symptom clusters; Cluster 1 (n=190) included cold (100%) and myalgia (28.9%); Cluster 2 (n=37), headache (100%) and myalgia (32.4%); Cluster 3 (n=33), GI symptoms (100%), general weakness, headache, and cold; Cluster 4 (n=34), cold (100%), myalgia, headache, and respiratory symptoms; Cluster 5 (n=241), respiratory symptoms (10.8%); Cluster 6 (n=76): myalgia (75.0%) and general weakness, and Cluster 7 (n=54), cold (87.0%), general weakness, and respiratory symptoms. Conclusion: The results of this comprehensive symptom assessment are hoped to be helpful in developing better symptom management for ER patients with fever than before. Further research is warranted to verify the symptom clusters of this study in different clinical settings.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.13 no.3
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• pp.36-45
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• 2010

To analyze the relationship between climatic factors (monthly mean temperature and total precipitation) and tree-ring growths of Quercus mongolica Fischer (Mongolian oak) with different topographic sites in Mt. Worak, more than 10 trees were selected from each of seven stands. Two cores from each tree were measured for ring width. After crossdating, each ring-width series was double standardized by fitting first a negative exponential or straight regression line and secondly a 60-year cubic spline. Seven stands were categorized in two groups using cluster analysis for tree-ring index patterns. Cluster I (four stands) was located in higher elevation (550-812 m) with aspects of east, west and northwest, and cluster II (three stands) was located in rather lower election (330-628 m) with aspects of north and northwest. The aspects of two clusters were not significantly different. Response-function analysis showed a significant positive response to March precipitation for both clusters. It indicates that moisture supply during early spring season is important to radial growth because the cambial growths of ring-porous species, such as Mongolian oak, start before leaf growth. Cluster II showed a positive response to the precipitation of middle and late growing season, too.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.31 no.4
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• pp.169-176
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• 2020

Exploring the risk factors of adolescent suicide is important for effective suicide prevention. This study explored the clustering of adolescent suicides based on six risk factors: mental disorder, broken family, depression, anxiety, previous suicide attempts, and deviant behaviors. Using 173 student suicide reports obtained from the Ministry of Education, we evaluated the associations between suicide and variables related to mental disorders; dysfunctional family life; depression and anxiety; previous suicide attempts; deviant behaviors such as drinking and smoking; and school life characteristics, including attendance and discipline, problems within the past year, and incidents prior to suicide. In addition, reports of warning signs just before suicide were included in the analysis. The two-stage cluster analysis classified the students into three clusters: the silent type (cluster 1; 48.55%), in which no risk factors were observed; environmental-risk type (cluster 2: 24.28%), which featured a high frequency of broken households, deviant behaviors such as smoking/drinking and running away from home; and depressive type (cluster 3: 27.17%), which featured a high frequency of mental health problems such as depression, anxiety, and suicide attempts. Identifying the sub-types of adolescent suicide may help to inform tailored suicide prevention and intervention strategies in school.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.387-394
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• 2007

This paper considers several stratification strategies for multivariate and multipurpose survey with several quantitative stratification variables. We propose three methods of stratification based on, respectively, the method of cumulative frequency square root which is the most popular one in univariate stratification, cluster analysis, and factor analysis followed by cluster analysis. We then compare the efficiency of those methods using the Dong-Eup-Myun data of the holding numbers of farming machines, extracted from the 2001 Agricultural Census. It turned out that the method based on cluster analysis with factor analysis would be a relatively satisfactory strategy.