• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3477-3485
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• 2012

This research focused on the cluster analysis for the vitalization of the innovation cluster, Gwanggyo Technovalley. The study was performed based on both theoretical study and quantitative and qualitative study approaches. Particularly, questionnaire survey was performed for the cluster analysis of the innovation cluster. The major determinants for vitalization of the innovation cluster, Gwanggyo Technovalley can be summarized as follows; the strategy formulation for the development of the innovation cluster, the enhancement of the host institution capability and gradual enlargement of the role of the host institution. In terms of the needs of times, this study regarding the cluster analysis for the vitalization of the innovation cluster, Gwanggyo Technovalley is anticipated to be a good reference for the R&D organizations and technology cluster participants in coming years.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.3
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• pp.79-99
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• 2020

The purpose of this study is to investigate the model of cluster analysis techniques for grouping public libraries and analyze their characteristics. Statistical data of public libraries of the National Library Statistics System were used, and three models of cluster analysis were applied. As a result of the study, cluster analysis was conducted based on the size of public libraries, and it was largely divided into two clusters. The size of the cluster was largely skewed to one side. For grouping based on size, the ward method of hierarchical cluster analysis and the k-means cluster analysis model were suitable. Three suggestions were presented as implications of the grouping method of public libraries. First, it is necessary to collect library service-related data in addition to statistical data. Second, an analysis model suitable for the data set to be analyzed must be applied. Third, it is necessary to study the possibility of using cluster analysis techniques in various fields other than library grouping.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.203-216
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• 2022

In the recent business environment, purchase patterns are changing around the influence of COVID-19 and the online market. This study analyzed cluster and correlation analysis based on purchase and product information. The cluster analysis of new methods was attempted by creating customer, product, and cross-bonding clusters. The cross-bonding cluster analysis was performed based on the results of each cluster analysis. As a result of the correlation analysis, it was analyzed that more association rules were derived from a cross-bonding cluster, and the overlap rate was less. The cross-bonding cluster was found to be highly efficient. The cross-bonding cluster is the most suitable model for recommending products according to customer needs. The cross-bonding cluster model can save time and provide useful information to consumers. It is expected to bring positive effects such as increasing sales for the company.

• Culinary science and hospitality research
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• v.17 no.2
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• pp.74-84
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• 2011

The purpose of this study is to understand the relationship between educational environment services offered and student satisfaction with them. A survey was conducted to 314 students in a cooking and food service program. To achieve the purpose of this study, SPSS 17.0, a statistics program, was used for a descriptive analysis, a frequency analysis, a factor analysis, a multiple regression analysis, a cluster analysis and a One-Way ANOVA analysis. The hypothesis test result of the effect of educational environment services on satisfaction showed that there was a partially significant effect in satisfaction with facilities and lecturers(p<0.01) as well as administrative services (p<0.01). In the cluster analysis of educational environment services, cluster 1 was classified as a mistry cluster, cluster 2 as a satisfied cluster, cluster 3 as a dissatisfied cluster and cluster 4 as a positive cluster. They showed a statistically meaningful result in the one-way ANOV A(p<0.01).

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.167-181
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• 2007

Many clustering algorithms and cluster validation techniques for high-dimensional gene expression data have been suggested. The evaluations of these cluster validation techniques have, however, seldom been implemented. In this paper we compared various cluster validity indices for low-dimensional simulation data and real gene expression data, and found that Dunn's index is the most effective and robust, Silhouette index is next and Davies-Bouldin index is the bottom among the internal measures. Jaccard index is much more effective than Goodman-Kruskal index and adjusted Rand index among the external measures.

• Journal of Korea Technology Innovation Society
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• v.6 no.2
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• pp.175-190
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• 2003

This paper deals with the methods for selecting the key research areas, which fit for the large, multi-disciplinary, and long-term programs by making use of Technology Cluster Analysis. This method is applied to mano-technology field at the level of national R&D program. 56 nano-technologies are analyzed and grouped into three main clusters based on the survey data from 180 experts. Three main clusters are \circled1 naro-materials related cluster, \circled2 naro-device related cluster, and \circled3 naro-bio related cluster. These three clusters are coincided with the focused areas of nano-technology in Korea. Each cluster is analyzed in view of its competence position.

• Journal of the Korean Society of Clothing and Textiles
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• v.18 no.2
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• pp.211-220
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• 1994

The purpose of this study was to analyze the foot type by cluster analysis for footwear. The sample size for the study was 200 college womens between age 19 and 23 in Pusan urban area. There were measured 17 items of the foot for factor analysis and cluster analysis. The result was as follows : 1. 1'here were 9 items selected by factor analysis. 2.'rho cluster analysis of the foot must be analyzed by direct and indirect measurement indivisually. 3. The cluster analysis of the direct measurement ; Cluster 1 : 1'he foot length is all much the same to mean value of this age group and the items of width and circumference are relatively small to other clusters. Cluster 2 ; The foot length is relatively small to other clusters and the items of width and circumference are all much the same to mean value of this age group. Cluster 3 ; The foot sine Is relatively large to other clusters. 4. The cluster analysis of indirect measurement ; Cluster 1 ; The (cot print angle is high find Metatarso-Phalanx angle is transformed Cluster 2 ; The foot print angle is low and Melatarso-Phalanx angle is normal. Cluster 3 : Tho foot print angle Is middle and Metatarso-Phalanx angle is all the mush same to mean value of this age group. Cluster 4 . The foot print angle Is the most value and Metatarso-Phalanx angle is normal.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.905-922
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• 2021

Cluster analysis is one of unsupervised learning techniques used for discovering clusters when there is no prior knowledge of group membership. K-means, one of the commonly used cluster analysis techniques, may fail when the number of variables becomes large. In such high-dimensional cases, it is common to perform tandem analysis, K-means cluster analysis after reducing the number of variables using dimension reduction methods. However, there is no guarantee that the reduced dimension reveals the cluster structure properly. Principal component analysis may mask the structure of clusters, especially when there are large variances for variables that are not related to cluster structure. To overcome this, techniques that perform dimension reduction and cluster analysis simultaneously have been suggested. This study proposes probabilistic reduced K-means, the transition of reduced K-means (De Soete and Caroll, 1994) into a probabilistic framework. Simulation shows that the proposed method performs better than tandem clustering or clustering without any dimension reduction. When the number of the variables is larger than the number of samples in each cluster, probabilistic reduced K-means show better formation of clusters than non-probabilistic reduced K-means. In the application to a real data set, it revealed similar or better cluster structure compared to other methods.

• Journal of the Korean Society of Clothing and Textiles
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• v.18 no.5
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• pp.637-645
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• 1994

The purpose of this study was to analyze the characteristics of men's foot and the foot type by cluster analysis for men's footwear. The sample size for the study was 200 college men between age 19 and 24 in Pusan urban area. There were measured 17 items of the foot for factor analysis and cluster analysis. The result was as follows: 1. The size of If items of men's foot is larger than women's foot. 2. There were 9 items selected by factor analysis. 3. The cluster analysis of the direct measurement: Cluster 1: The items of the direct measurement is all much the same to mean value of this age group. Cluster 2: The foot size is relatively small to other clusters. Cluster 3: The foot size is relatively large to other clusters. 4. The cluster analysis of indirect measurement: Cluster 1: The foot print angle is the most value and Metatarso-Phalanx angle is nomral Cluster 2: The foot print angle is middle and Metatarso-Phalanx angle is normal. Cluster 3: The foot print angle is high and Metatarso-Phalanx angle is the smallest. Cluster 4: The foot print angle is low and Metatarso-Phalanx angle is all the much same to mean value of this age group.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.63-76
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• 2006

Several clustering algorithms to analyze gene expression data and cluster validation techniques that assess the quality of their outcomes, have been suggested, but evaluations of these cluster validation techniques have seldom been implemented. In this paper we compared various cluster validity indices for simulation data and real genomic data, and found that Dunn's index is more effective and robust through small simulations and with real gene expression data.