• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1675-1683
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• 2011

The cluster project in Korea has restructured to develop the cluster into a core engine for the pan-regional development policy and connect industrial parks across the country. Korean government has made great efforts to attract research institutes, support organizations and universities in industrial parks and enhance growth potential through a close cooperation network. Especially A mini-cluster is an integrated group joined by companies, universities and research institutes and support organizations. It has been developing business opportunities through industry-academia-research cooperation and providing assistance through network activities. Mini clusters have implemented a variety of network activities. This paper proposed three strategies to accomplish self-sustainable growth of mini-clusters. First of all, mini-cluster needs to provide useful contents that can attract the participation of members. Then it is necessary to promote mini-cluster initiative. Finally, mini-clusters need to make up a cooperative culture.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.928-938
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• 2008

This study presents an approach for approximation an unknown function from a numerical data set based on the synthesis of a neuro-fuzzy model. An adaptive input data space parting method, which is used for building hyperbox-shaped clusters in the input data space, is proposed. Each data cluster is implemented here as a fuzzy set using a membership function MF with a hyperbox core that is constructed from a min vertex and a max vertex. The focus of interest in proposed approach is to increase degree of fit between characteristics of the given numerical data set and the established fuzzy sets used to approximate it. A new cutting procedure, named NCP, is proposed. The NCP is an adaptive cutting procedure using a pure function $\Psi$ and a penalty function $\tau$ for direction the input data space parting process. New algorithms named CSHL, HLM1 and HLM2 are presented. The first new algorithm, CSHL, built based on the cutting procedure NCP, is used to create hyperbox-shaped data clusters. The second and the third algorithm are used to establish adaptive neuro- fuzzy inference systems. A series of numerical experiments are performed to assess the efficiency of the proposed approach.

• Korean Journal of Human Ecology
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• v.22 no.4
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• pp.575-591
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• 2013

In this paper the relationship of various types of work-family interaction (i.e. work-family conflict, and work-family enhancement) with individual, family, and employment characteristics was explored in a sample of 1000 married employed women with preschool children. By using cluster analysis, we tried to reveal whether specific combinations of the various dimensions of work-family interaction (WFI) exist. Our results showed that employed women did not simply experience work-family conflict or work-family enhancement, but that they should be classified in four distinct clusters: (1) 189 employed women experienced primarily work-family enhancement(i.e. positive WFI); (2) 289 employed women experienced primarily work-family conflict(i. e. negative WFI); (3) 338 employed women experienced work-family conflict and work-family enhancement simultaneously(i. e. both positive and negative WIF); (4) 184 employed women did not experience either work-family conflict or work-family enhancement(i. e. low WFI). Results further showed that the emerging WFI-clusters appeared to have distinct profiles with respect to individual, family and employment characteristics.

• Journal of the Korean Academic Society of Industrial Cluster
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• v.3 no.1
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• pp.26-38
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• 2009

Many economic development practitioners view cluster theory and analysis as constituting a general approach to strategy making in economic development, which may lead them to prioritize policy and planning interventions that cannot address the actual development challenges in their cities and regions. This paper discusses the distinction between strategy formation and strategic planning, where the latter is the programming of development strategies that are identified through a blend of experience, intuition, and analysis. Cluster theories and analytical tools can provide useful informational inputs into a strategy making effort and they can also be helpful for programming specific interventions (i.e., strategic planning). However, they should not be used as the exclusive or even predominant framework for filtering information about the competitive advantages of a region or for formulating strategy. To do so forces strategy making into a conceptual box defined by only one highly stylized theory of regional growth and development.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.8
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• pp.418-428
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• 2005

In this paper, a new probabilistic generation modeling method which can address the characteristics of changed electricity industry is proposed. The major contribution of this paper can be captured in the development of a probabilistic generation modeling considering generator maintenance outage and in the classification of market demand into multiple demand clusters for the applications to electricity markets. Conventional forced outage rates of generators are conceptually combined with maintenance outage of generators and, consequently, effective outage rates of generators are newly defined in order to properly address the probabilistic characteristic of generation in electricity markets. Then, original market demands are classified into several distinct demand clusters, which are defined by the effective outage rates of generators and by the inherent characteristic of the original demand. We have found that generators have different effective outage rates values at each classified demand cluster, depending on the market situation. From this, therefore, it can be seen that electricity markets can also be classified into several groups which show similar patterns and that the fundamental characteristics of power systems can be more efficiently analyzed in electricity markets perspectives, for this classification can be widely applicable to other technical problems in power systems such as generation scheduling, power flow analysis, price forecasts, and so on.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.804-806
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• 2005

In this paper, a new probabilistic generation modeling method which can address the characteristics of changed electricity industry is proposed. The major contribution of this paper can be captured in the development of a probabilistic generation modeling considering generator maintenance outage and in the classification of market demand into multiple demand clusters for the applications to electricity markets. Conventional forced outage rates of generators are conceptually combined with maintenance outage of generators and, consequently, effective outage rates of generators are new iy defined in order to properly address the probabilistic characteristic of generation in electricity markets. Then, original market demands are classified into several distinct demand clusters, which are defined by the effective outage rates of generators and by the inherent characteristic of the original demand. We have found that generators have different effective outage rates values at each classified demand cluster, depending on the market situation. From this, therefore, it can be seen that electricity markets can also be classified into several groups which show similar patterns and that the fundamental characteristics of power systems can be more efficiently analyzed in electricity markets perspectives, for this classification can be widely applicable to other technical problems in power systems such as generation scheduling, power flow analysis, price forecasts, and so on.

• Journal of Technology Innovation
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• v.13 no.2
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• pp.207-222
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• 2005

A prerequisite for making R&D more productive is to able to measure its productivity. Most of the previous studies on this topic have attempted to measure R&D productivity at the firm or industry levels. In this study, however, R&D productivity is measured at the national level to provide R&D policy implications, particularly for Asian countries. Contrary to the previous studies where total factor productivity was adopted, this study employs the data envelopment analysis (DEA) approach to measure R&D productivity. DEA is a multi-factor productivity analysis model for measuring the relative efficiency of each Decision Making Unit (DMU). In addition to the basic DEA model that includes all inputs and outputs, five additional models are constructed by combining single input with all outputs and single output with all inputs in order to measure specialized R&D efficiency. In this study, the twenty-seven countries are classified into four clusters based on the output-specialized R&D efficiency: inventors, merchandisers, academicians, and duds. Then, the characteristics of the Asian countries with respect to R&D efficiency are identified. It is found that Singapore ranks high in total efficiency, and Japan in patent-oriented efficiency. Meanwhile, China, Korea, and Taiwan are found to be relatively inefficient in R&D. We expect that the findings from this study will be able to provide directions for R&D policy-making of the Asian countries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.976-990
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• 2020

The k-means algorithm is widely used in academia and industry due to easy and simple implementation, enabling fast learning for complex datasets. However, k-means struggles to classify datasets without prior knowledge of specific domains. We proposed the repulsive k-means (RK-means) algorithm in a previous study to improve the k-means algorithm, using the repulsive force concept, which allows deleting unnecessary cluster centroids. Accordingly, the RK-means enables to classifying of a dataset without domain knowledge. However, three main problems remain. The RK-means algorithm includes a cluster repulsive force offset, for clusters confined in other clusters, which can cause cluster locking; we were unable to prove RK-means provided optimal convergence in the previous study; and RK-means shown better performance only normalize term and weight. Therefore, this paper proposes the advanced RK-means (ARK-means) algorithm to resolve the RK-means problems. We establish an initialization strategy for deploying cluster centroids and define a metric for the ARK-means algorithm. Finally, we redefine the mass and normalize terms to close to the general dataset. We show ARK-means feasibility experimentally using blob and iris datasets. Experiment results verify the proposed ARK-means algorithm provides better performance than k-means, k'-means, and RK-means.

• Fashion & Textile Research Journal
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• v.4 no.1
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• pp.56-63
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• 2002

This study was designed to investigate the new concept about lifestyle of female consumer in the present time of digital revolution and to analyze the preferences and sensibilities according to the types of consumer lifestyle classified into the same group. Survey was done through questionnaire of 79 questions and the data of 151 female consumers in the age of 19-34 were analyzed statistically using SPSS. The 6 factors were extracted from 39 lifestyle questions: consumerism, seeking challenge, communal life, quality of life, digital orientation and active counter plan. 6 Lifestyle clusters of female consumers were as following: the no-concern satisfied, the digital passive, the consumer personal, the digital active, the consumer communal and the adventurous. 30.5% of female consumer was the digital lifestyle who are relatively older and highly educated, of high income and expense rate, and resident in Gangnam. The preferred sensations by female consumer were 5; reasonal, feminine, conspicuous, active, and modest, and the clusters according to the sensations were 5: the casual, the status-symbolism, the rich in contents, the romantist, and the elegance. Lifestyle and preferred sensations were so related that the no-concern satisfied were the status-symbolism and the romantist, while the digital were the richness of contents and the adventurous were the romantist.

• Journal of the Korean Society of Food Culture
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• v.23 no.3
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• pp.377-387
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• 2008

The objective of this study was to use cluster analysis to determine differences in eating-out behavior among grouped clusters of female consumers after each cluster was divided based on lifestyle patterns. The data were collected by interview survey from a biased sample of 1,300 females, ranging from ages 20 to 59, and living in residential districts of Seoul. Reliability analysis, factor analysis, cluster analysis, cross-tabulation analysis, and analysis of variance (ANOVA) were applied to the data. Four lifestyle factors were extracted by lower-division and classified as follows: health condition, consuming, food, and housing lifestyles. Based on these four factors, the female consumers were grouped as three clusters: the consuming-individuality type, rational-pursuit type, and conservative-stability type. The eating-out behavior of each cluster was significantly different in terms of frequency of eating-out, eating-out expenditures, restaurant selection criteria, food preferences, and the purpose for eating-out. Since this study surveyed females from ages 20 to 59, age and demographics were the differential factors in determining the various lifestyle types. Thus, to target the consumers who form a target market, the food industry should consider market segmentation that combines demographic factors such as age, income, and marital status.