• Cartoon and Animation Studies
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• s.28
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• pp.209-233
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• 2012

The animation cluster in Korea has begun as a part of the local cultural cluster in the late 1990s with components of companies, local governments, educational institutions, and human resources, which were necessary to run the cluster. And, the animation cluster was initiated for the purpose of encouraging regional economies, but the basic unit was the local on a small scale. Because of this inherent weakness in the local cluster, it was needed to add some additional strategies that could expand the local animation industry into the formal leading industry. That is why the development policy was set up, and the local promotion agency based on this policy was established. It has been several years to manage the local promotion agency, and it is reported that there have been some visible effects. But, it is found out that analyzing the outcome of small unit cluster on the basis of existing criteria on a large scale is not reliable, which means it is not possible to evaluate the outcome of local cluster in a material way. Some examples of problems are as follows. First, the local cluster was made not autonomously but deliberately. Second, the animation cluster of each province has the same market as its target. Therefore, this research - on the basis of the diamond model - focuses on analyzing the existence and role of local promotion agencies rather than evaluating the outcome itself. Through the cases of two provincial cities, Chuncheon and Bucheon, this research examines if it is possible to evaluate the outcome of local clusters managed by promotion agencies.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.4
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• pp.688-701
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• 2014

This article is focused on the agglomeration and decline factors of the footwear industries in Busan metropolitan area from the industrial cluster point of perspective. For the research, 'the components and network of industrial cluster model' are presented which is restructured of M. Porter's cluster model. Moreover, this research have examined the agglomeration and decline process of the footwear industries in Busan area and conducted a survey targeting footwear enterprises in Busan area. In the late 1980's, the footwear industries in Busan area formed the largest footwear industrial cluster in the world. However, the industrial cluster started to decline from early 1990's and now it is reduced in to 1/10 size of the past. The growth factors of Busan footwear industrial cluster include cheap and plentiful labours, penetration of OEM production, entrepreneur spirit, human resources network, government's support and so on. Moreover, the agglomeration of relative companies also created high competitiveness in this cluster. The decay factors are pointed out sudden rise of labour cost, shortage of factory site, rise of land price, alteration of government policy, international relocation of footwear production and growth of overseas industrial cluster. Busan footwear industrial cluster nowadays has declined in its size, but it is the only footwear industrial cluster in Korea.

• Journal of Korea Technology Innovation Society
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• v.10 no.4
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• pp.808-841
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• 2007

Recently some researchers started to discuss the fundamental limitations of geographical innovation clusters. With the rapid development of information and telecommunication technologies, a new concept of virtual innovation cluster is emerging in the advanced countries an alternative to overcome the limitations of geographical clusters. In this context, this study is designed as a pilot study to verify limitations of and issues with geographical innovation cluster through a survey and review the possible application of virtual innovation cluster as an alternative to geographical innovation cluster. Topics covered in this study include; 1) What are the limitations and/or issues with geographical clusters? 2) Can these limitations of and issues with geographical innovation cluster be overcome with virtual innovation cluster? 3) What is the government policy direction to ensure effective utilization of virtual innovation clusters? To answer these questions, this study adopted various research techniques including literature reviews, surveys, case studies, and expert interviews. Especially, a survey was conducted to diagnose the limitations of geographical innovation cluster policy and to understand the possibility of applying virtual innovation clusters. Finally, the significances and limitations of the study are discussed.

• 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.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.8
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• pp.1386-1397
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• 2001

The purpose of this study was to classify and analyze the somatotype of early middle-aged women and to provide its total data for clothing construction, and to improve clothing culture. The subjects were 277 early middle-aged women between 35 and 44 years old. Data were collected through anthropometry and photometry and analyzed by factor analysis, cluster analysis and discriminant analysis. The results were as follows; 1. The result of factor analysis indicated that 10 factors were extracted through factor analysis and those factors comprised 86.13 percent of total variance. 2. Using factor scores, cluster analysis was carried out and the subject were classified into 4 cluster. Type 1 is tall, slim, and X type in front. Type 2 is standard height and weight, short upper body, and hip-protruded on the side. Type 3 is standard height, thin, H type in front, back and hip are clearly protruded, and lean-back type on the side. Type 4 is standard height, fat, and long upper body. 3. According to the stepwise discriminant analysis, the 8 important iems is classifying the somatotype of early middle-aged women are as follows : bust girth, back length hip breadth-waist breadth, back protruded point depth(back)-back waist depth(back), hip tangent tilt, hip depth(back) waist dapth(back), bust depth-waist depth, and cervical hight, The correct classification rate for these items is as exact as 83.20%.

• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.282-292
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• 2019

The purpose of this study was to investigate the relationship between self-esteem and differentiation of self, and to identify the degree of self-esteem and differentiation of self. Data were collected from 151 elementary school students using community child center. Factor analysis, reliability analysis, correlation analysis, regression analysis, and cluster analysis were performed. The results of the study are as follows: First, as a result of factor analysis, differentiation of self was classified into differentiation of internal mind, other's and self oriented. Self-esteem was classified into isolation, achievement, relation, and daunted. Second, regression analysis indicated that there was obvious relationship between self-esteem and the differentiation of self. Third, cluster analysis showed that 37.6% of children belonged to cluster 2 with achievement and relation patterns and self-oriented differentiation. Forty-three percent were belonged to cluster 1 with isolated and daunted self-esteem as well as internal mind and other's oriented differentiation. In cluster 3, 19.4% were belonged and their levels of self-esteem and the differentiation of self were very low. Findings indicate that for those children belonged to cluster 1 and 3, programs to raise the self-esteem of children and parental education are needed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.132-145
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• 2022

This study was based on the black box images of traffic accidents on highways, cluster analysis and prediction model comparisons were carried out. As analysis data, vehicle driving behavior and road surface conditions that can grasp road and traffic conditions just before the accident were used as explanatory variables. Considering that traffic accident data is affected by many factors, cluster analysis reflecting data heterogeneity is used. Each cluster classified by cluster analysis was divided based on the ratio of the severity level of the accident, and then an accident prediction evaluation was performed. As a result of applying the Logit model, the accident prediction model showed excellent predictive ability when classifying groups by cluster analysis and predicting them rather than analyzing the entire data. It is judged that it is more effective to predict accidents by reflecting the characteristics of accidents by group and the severity of accidents. In addition, it was found that a collision accident during stopping such as a secondary accident and a side collision accident during lane change act as important driving behavior variables.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.87-97
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• 2024

This study analyzed the water shortage hotspot areas in South Korea using spatial clustering analysis for water shortage estimates in 2030 of the Master Plans for National Water Management. To identify the water shortage cluster areas, we used water shortage data from the past maximum drought (about 50-year return period) and performed spatial clustering analysis using Local Moran's I and Getis-Ord Gi*. The areas subject to spatial clusters of water shortage were selected using the cluster map, and the spatial characteristics of water shortage areas were verified based on the p-value and the Moran scatter plot. The results indicated that one cluster (lower Imjin River (#1023) and neighbor) in the Han River basin and two clusters (Daejeongcheon (#2403) and neighbor, Gahwacheon (#2501) and neighbor) in the Nakdong River basin were found to be the hotspot for water shortage, whereas one cluster (lower Namhan River (#1007) and neighbor) in the Han River Basin and one cluster (Byeongseongcheon (#2006) and neighbor) in the Nakdong River basin were found to be the HL area, which means the specific area have high water shortage and neighbor have low water shortage. When analyzing spatial clustering by standard watershed unit, the entire spatial clustering area satisfied 100% of the statistical criteria leading to statistically significant results. The overall results indicated that spatial clustering analysis performed using standard watersheds can resolve the variable spatial unit problem to some extent, which results in the relatively increased accuracy of spatial analysis.

• Journal of Korean Society of Rural Planning
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• v.12 no.1 s.30
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• pp.37-48
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• 2006

The objectives of this study were to develop an evaluation method of regional vulnerability to agricultural drought and to classify the vulnerability patterns. In order to test the method, 24 city or county areas of Gyeonggi-do were chose. First, statistic data and digital maps referred for agricultural drought were defined, and the input data of 31 items were set up from 5 categories: land use factor, water resource factor, climate factor, topographic and soil factor, and agricultural production foundation factor. Second, for simplification of the factors, principal component analysis was carried out, and eventually 4 principal components which explain about 80.8% of total variance were extracted. Each of the principal components was explained into the vulnerability components of scale factor, geographical factor, weather factor and agricultural production foundation factor. Next, DVIP (Drought Vulnerability Index for Paddy), was calculated using factor scores from principal components. Last, by means of statistical cluster analysis on the DVIP, the study area was classified as 5 patterns from A to E. The cluster A corresponds to the area where the agricultural industry is insignificant and the agricultural foundation is little equipped, and the cluster B includes typical agricultural areas where the cultivation areas are large but irrigation facilities are still insufficient. As for the cluster C, the corresponding areas are vulnerable to the climate change, and the D cluster applies to the area with extensive forests and high elevation farmlands. The last cluster I indicates the areas where the farmlands are small but most of them are irrigated as much.

• The KIPS Transactions:PartB
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• v.18B no.2
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• pp.67-72
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• 2011

Single nucleotide polymorphisms (SNPs) are a very important tool for the study of human genome structure. Cluster analysis of the large amount of gene expression data is useful for identifying biologically relevant groups of genes and for generating networks of gene-gene interactions. In this paper we compared the clusters of SNPs within asthma group and normal control group obtained by using hierarchical cluster analysis method with entropy distance. It appears that the 5-cluster collections of the two groups are significantly different. We searched the best set of SNPs that are useful for diagnosing the two types of asthma using representative SNPs of the clusters of the asthma group. Here support vector machines are used to evaluate the prediction accuracy of the selected combinations. The best combination model turns out to be the five-locus SNPs including one on the gene ALOX12 and their accuracy in predicting aspirin tolerant asthma disease risk among asthmatic patients is 66.41%.