• 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.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.89-96
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• 2007

Various classification methods have been developed to reuse components. These classification methods enable the user to access the needed components quickly and easily. Conventional classification approaches include the following problems: a labor-intensive domain analysis effort to build a classification structure, the representation of the inter-component relationships, difficult to maintain as the domain evolves, and applied to a limited domain. In order to solve these problems, this paper describes a composite cluster analysis approach for component classification. The cluster analysis approach is a combination of a hierarchical cluster analysis method, which generates a stable clustering structure automatically, and a non-hierarchical cluster analysis concept, which classifies new components automatically. The clustering information generated from the proposed approach can support the domain analysis process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1801-1810
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• 2016

The OpenDaylight controller has been receiving significant attention as one of the enabling open source framework for SDN, and this paper analyzes the architecture and procedure of OpenDaylight based controller cluster. The OpenDaylight controller cluster uses shard based distributed datastore and Raft algorithm to maintain consistency among controllers inside a cluster. The performance evaluation analyzes the leader re-election time as well as latencies of CRUD and Routed RPC according to cluster size, shard role, and sharding strategy, and we discuss the optimal operation strategy for OpenDaylight controller cluster.

• Journal of the Economic Geographical Society of Korea
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• v.20 no.2
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• pp.189-213
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• 2017

This study seeks to critically examine the significance and limits of the cluster adaptive cycle model for analysis of cluster evolution and to propose research issues for future analysis of cluster evolution based on this critical examination. Until the 1980s, research on industrial complexes including clusters was based on a 'static perspective' that focuses on the aspect of economic space at a specific point in time, but the research paradigm has recently shifted to a 'dynamic perspective' focusing on 'evolution' of 'complex adaptive systems'. As a result, the adaptive cycle model has attracted attention as an analysis tool of dynamically evolving clusters. However, the cluster adaptive cycle model has emerged by being appropriately modified and expanded according to the properties of the cluster and its evolution. The cluster adaptive cycle model is a comprehensive analysis framework that identifies the characteristics of cluster evolution in terms of resource accumulation, interdependence, and resilience and classifies cluster evolution paths into six different categories. Nevertheless, there is still a need for further discussion and supplementation in terms of theoretical and empirical research to expand and deepen the model. Therefore, research issues for future analysis of cluster evolution are to specify and elaborate the cluster evolution model, to emphasize the concept of resilience, and to verify the applicability and usefulness of the model through empirical research.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.471-480
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• 2010

Cluster analysis is one of the useful methods to find out number of groups and member’s belongings. With the rapid development of computer application in statistics, variety of new methods in clustering analysis were studied such as EM algorism and Self organization maps. The goals of cluster analysis is finding the number of groupings that are meaningful to me. If data are analyzed perfectly with cluster analysis, we can get the same results from discernment analysis.

• Proceedings of the KAIS Fall Conference
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• 2012.05a
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• pp.254-257
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• 2012

본 논문은 혁신클러스터 추진의 전략방향 설정을 위해서 클러스터 현황을 클러스터 분석을 통하여 살펴보았다. 본 논문에서는 광교테크노밸리의 발전단계에 따른 협력현황 파악을 위하여 산학연 협력체계 분석과 진단을 위한 클러스터분석(Cluster Analysis)을 실시하였다. 클러스터 분석결과 광교테크노밸리내 입주기업의 산학연 협력경험은 67.3%로 매우 높은 것으로 나타났다. 대학과, 기업과는 연구개발, 연구기관과는 장비활용 중심으로 협력하고 있었다. 또한, 산학연 협력의 지원정책수요는 협력기관 현황제공, 기술별 정보취득지원 등 다양한 부문에서 협력의 수요가 존재하고 있었다. 이러한 분석결과를 토대로 다음의 전략이 도출되었다. 첫째, 혁신클러스터 발전을 위한 새로운 비전을 조속히 제시하고 단지인근에서 입주기업의 산학연 협력의 활성화를 위한 지원을 추진해야 한다. 둘째, 혁신클러스터 단계별 발전을 위한 통합적인 지원역량 강화가 필요하다. 마지막으로 인근의 타 혁신거점과 정책적 네트워크 구축을 통해서 타 혁신클러스터의 자원과 역량을 활용할 수 있는 지원망 구축이 필요하다.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.35-49
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• 2001

Over the years, cluster analysis has become a popular tool for marketing and segmentation researchers. There are various methods for cluster analysis. Among them, K-means partitioning cluster analysis is the most popular segmentation method. However, because the cluster analysis is very sensitive to the initial configurations of the data set at hand, it becomes an important issue to select an appropriate starting configuration that is comparable with the clustering of the whole data so as to improve the reliability of the clustering results. Many programs for K-mean cluster analysis employ various methods to choose the initial seeds and compute the centroids of clusters. In this paper, we suggest a methodology to evaluate various clustering programs. Furthermore, to explore the usability of the methodology, we evaluate four clustering programs by using the methodology.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.4
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• pp.112-123
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• 2015

The purpose of this study is to analyze the biotope characteristics of Korean cities and set up biotope type structures for Korean cities based on biotope type classification, dominant biotope type, city's human and nature environmental characteristics and cluster analysis. The findings of the study are summarized as follows: First, regarding the analysis of biotope type classification, cities showed differences in terms of the standard of biotope classification and classification hierarchy. Next, the analysis of dominant biotope types showed the type of forest represents the largest area in most cities. Moreover, a city's characteristic analysis revealed large differences between cities. As a result of cluster analysis, cities were classified into five clusters overall. First, Cluster A showed a lower population level and urbanization level. Unlike other cities, Cluster A revealed that it has the largest percentage of agricultural areas. Cluster C showed very high levels in terms of population amount and urbanization conditions was named the 'Large-sized metropolitan cities-center of forest biotope area' based on it's characteristics. The findings of this study as summarized above are considered to play an important role in enabling detailed classification and preservation of biotope types fit for the characteristics of cities and minimizing the confusion caused by different biotope mapping methods when revising and complementing biotope maps.

• Journal of The Korean Association For Science Education
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• v.25 no.3
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• pp.321-326
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• 2005

The purpose of this study was to identify distinctive achievement goal patterns of students and examine their influence on learning strategies (deep/surface) and science achievement. Cluster analysis procedure was performed to classify students on the basis of task, performance, and performance-avoidance goal scores. The results produced 3 clusters of students with different achievement goal patterns: high task goal (cluster 1), high task-high performance goal (cluster 2), and low task-low performance goal (cluster 3). One-way ANOVA results revealed that the scores of cluster 2 were significantly higher than those of clusters 1 and 3 in deep learning strategy. The science achievement test scores of clusters 1 and 2 were higher than those of cluster 3. Looking at surface learning strategy, however, the test scores of cluster 3 were significantly higher than those of clusters 1 and 2. The educational implications of these findings are discussed.

• Journal of Korea Technology Innovation Society
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• v.13 no.4
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• pp.738-757
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• 2010

Generally, it is known that the agglomeration economies appear in some industry clusters which were developed naturally. But, in Korea, most of industry clusters were built by government. This research was carried out to evaluate the performance of governments zoning investment, in other words, industry cluster policy. In this research, we use the data of manufacturing firms in Kyunggi-province. For the microeconomic analysis, we use the KIS-VALUE data of 2008. As the empirical test methods we use both multiple regressions and Propensity Score Matching. In conclusion, there is no evidences that firms in industry cluster have better performances. Surprisingly, in PSM analysis, we find the evidence that firms in industry cluster show less innovative performance.