• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.45.2-45.2
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• 2017

Using the latest cosmological hydrodynamic N-body simulations of groups and clusters, we study how location in phase-space coordinates at z = 0 can provide information on environmental effects acting in clusters. We confirm the results of previous authors showing that galaxies tend to follow a typical path in phase-space as they settle into the cluster potential. As such, different regions of phase-space can be associated with different times since first infalling into the cluster. However, in addition, we see a clear trend between total mass loss due to cluster tides and time since infall. Thus, we find location in phase-space provides information on both infall time and tidal mass loss. We find the predictive power of phase-space diagrams remains even when projected quantities are used (i.e.,line of sight velocities, and projected distances from the cluster). We provide figures that can be directly compared with observed samples of cluster galaxies and we also provide the data used to make them as supplementary data to encourage the use of phase-space diagrams as a tool to understand cluster environmental effects. We find that our results depend very weakly on galaxy mass or host mass, so the predictions in our phase-space diagrams can be applied to groups or clusters alike, or to galaxy populations from dwarfs up to giants.

• Journal of Korea Technology Innovation Society
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• v.20 no.4
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• pp.970-988
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• 2017

As an innovation cluster has become an important policy tool for attaining regional and national competitiveness, many nations and regions are making great efforts to foster innovation clusters. In the case of Korea also, many innovation clusters have been established and some of them are recently receiving a lot of attention due to their performance. In fact, there have been lots of investment by the central and regional governments. However, there have been no in-depth analyses on Korean successful innovation clusters. This study investigates some of the success factors of a Korean representative innovation cluster, Pangyo Techo Valley. We find out that the Pangyo Techno Valley has four groups of success factors: innovative environment, consistent policy support by regional government, knowledge networks, and good feed-back system of innovation. Our findings would have some theoretical and practical implications for innovation cluster research and policy practice.

• 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.20 no.2
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• pp.425-433
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• 2009

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. We need a data mining tool to explore a lot of information. There are many data mining tools or solutions; E-Miner, Clementine, WEKA, and R. Almost of them are were focused on diversity and general purpose, and they are not useful for laymen. In this paper we design and implement a web-based data mining tool using PHP and WEKA. This system is easy to interpret results and so general users are able to handle. We implement Apriori algorithm of association rule, K-means algorithm of cluster analysis, and J48 algorithm of decision tree.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.75-84
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• 2022

The efficiency of the regional fiscal policy implementation is based on the achievement of target criteria in the formation and distribution of own financial resources of local budgets, reducing their deficit and reducing dependence on transfers. It is also relevant to compare the development of financial autonomy of regions in the course of decentralisation of fiscal relations. The study consists in the cluster analysis of the effectiveness of fiscal policy implementation in the context of 24 regions and the capital city of Kyiv (except for temporarily occupied territories) under conditions of fiscal decentralisation. Clustering of the regions of Ukraine by 18 indicators of fiscal policy implementation efficiency was carried out using Ward's minimum variance method and k-means clustering algorithm. As a result, the regions of Ukraine are grouped into 5 homogeneous clusters. For each cluster measures were developed to increase own revenues and minimize dependence on official transfers to increase the level of financial autonomy of the regions. It has been proved that clustering algorithms are an effective tool in assessing the effectiveness of fiscal policy implementation at the regional level and stimulating further expansion of financial decentralisation of regions.

• Journal of the Economic Geographical Society of Korea
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• v.12 no.4
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• pp.477-493
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• 2009

This research aims to examine Korean regional innovation governance, find structural problems, and explore alternative strategies of regional innovation governance. Especially the alternative governance was searched through the case study of Daegu-Kyungbuk mobile cluster, of which formulation Samsung is the anchor institution. Regional innovation governance in this research is defined as a policy system to link knowledge generation & diffusion subsystem and knowledge application & exploitation subsystem, and institutional conditions to steer the system. "Social Capital Assessment Tool (SOCAT)" of the World Bank was utilized for the appreciation of cluster governance. The regional innovation governance of Daegu-Kyungbuk mobile cluster is characterized as production networks dominated by one-to-one relationship between Samsung and hardware/software developers, decentralized R&D networks and policy networks with multiple hubs. Major policy agents have not developed networks with local companies, and rare are interactions between the policy agents. Local companies, especially software developers, responded they have had experiences to cooperate for local problem solving and shared their community goal, however, the degree of trust in major local project leaders is not high. Local hardware/software developers with core technologies need to be cooperative to develop similar technologies or products in Daegu-Kyungbuk mobile cluster. Regional administrative actors, such as the City of Daegu and Kyungsangbuk-do, and diverse innovation-related institutes should build cooperative environment where diverse project-based cooperation units are incessantly created, taken apart, and recreated.

• Proceedings of the Korean Vacuum Society Conference
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• 2004.08a
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• pp.135-135
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• 2004

• 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.

• Women's Health Nursing
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• v.3 no.2
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• pp.208-223
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• 1997

This study was made to suggest the nursing strategies for promoting the behaviors about bone mass health behaviors in order to prevent middle aged women's osteoporosis. This study was a descriptive-correlational design that also concerned to the types which improve bone mass promoting behaviors by inspecting patterns of health locus of control method out of recognizable variables of health improving models influencing on these bone mass promoting behaviors. For these purpose, data were collected by self reported questionnaire in middle school, from 158 women living in Seoul. The measuring tools used in this study about bone mass promoting behaviors and multidimensional health locus of control, were developed by author on the basis of literature review and analyzed by SPSS-PC window, into pearson's correlation, ANOVA, multiple regression, cluster analysis. Data was analyzed as follows. 1. 6 Multidimensional health locus of control scale clusters were existed. : a)cluster I (pure internal), b)cluster II(pure chance), c) cluster III(Believer in control), d), cluster IV(Type VI), e)cluster V(yea sayer), f) cluster VI(nay sayer). There were no findings of the powerful others external cluster and double external cluster. 2. The higher the value of internal health locus of control was, the better the bone mass promoting behaviors were(r=.2891, $p=.00^{**}$). The higher the value of chance external health locus of control was, the worse the bone mass promoting behaviors were(r=-.1367, $p=.00^{**}$). 3. On the basis of these relationships, 6 clusters were significantly different in the bone mass promoting behaviors(F=2.27, $p=.05^*$). The value of bone mass promoting behaviors was ranked the order of type VI>believer in control>pure internal>yea sayer>nay sayer>pure chance external highly. 4. Bone mass promoting behaviors were not significantly different as to age. Suggestion. Based on the results from the study, I would like to make some suggestions as follows. 1) To delay the loss of bone mass in middle aged women, the study on the cluster of the multidimensional health locus of control should be conducted repeatedly. 2) The tool of multidimensional health locus of control should be developed through a qualitative method adjusted on Korean' health culture.

• Genomics & Informatics
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• v.4 no.4
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• pp.173-181
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• 2006

Pattern finding is one of the important tasks in a protein or DNA sequence analysis. Alignment is the widely used technique for finding patterns in sequence analysis. BLAST (Basic Local Alignment Search Tool) is one of the most popularly used tools in bio-informatics to explore available DNA or protein sequence databases. BLAST may generate a huge output for a large sequence data that contains various sequence patterns. However, BLAST does not provide a tool to summarize and analyze the patterns or matched alignments in the BLAST output file. BLAST lacks of general and robust parsing tools to extract the essential information out from its output. This paper presents a pattern summary system which is a powerful and comprehensive tool for discovering pattern structures in huge amount of sequence data in the BLAST. The pattern summary system can identify clusters of patterns, extract the cluster pattern sequences from the subject database of BLAST, and display the clusters graphically to show the distribution of clusters in the subject database.