• Journal of Scientific & Technological Knowledge Infrastructure
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• s.20
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• pp.52-59
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• 2005

국가혁신체계로서 클러스터의 중요성과 더불어, 향후의 방향에 대해 논하고자 한다. 본 연구의 목적은 클러스터의 지역, 산업, 정책적인 특성을 파악하여서 미래의 발전방향을 파악해보기 위한 것이다. 일반적으로 클러스터는 지역적으로, 산업적으로 확산되는 가운데, 정책적 지원을 통한 성장을 기대하고 있다. 지역, 산업, 정책 클러스터로 구분하여 살펴본 결과, 지역 및 산업 간의 연결과 정책적 촉진(facilitate)은 불가피한 것으로 나타났다. 다만 정책은 클러스터의 진화에 필요한 합리적이고 객관적인 기준설정에 의해 실행되어야 하고, 궁극적으로 시장의 자율적 역할이 극대화되는 방향으로 전개되어야 할 필요성이 존재하고 있다. 따라서 클러스터 진화과정에서 시장의 자율 즉 새로운 비즈니스에 대한 시장의 엄격한 평가가 진행될 수 있도록 진화시키는 것이 중요하다고 하겠다.

• 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.

• Cartoon and Animation Studies
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• s.25
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• pp.155-175
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• 2011

The cultural cluster in Chuncheon was initiated in the late 1990s with the combination of diverse components, which were companies, a local government entity, educational institutions, and human resources. In order to hold ability of standing on its own way in a hard time when the local cultural industries could not had developed by themselves, it urgently required the encouragement policy in the name of industry development. GIMC (Gangwon Information & Multimedia Corporation) was established in this situation, and policy strategies for promoting the cultural cluster was decided on the basis of GIMC's strategies. This article analyzes the evolution process of the local cluster and suggests characteristics of its every stage--initiation, development, and decline--on the basis of cluster competition concept because of the problem to deal with the evolution process of cluster from the existing viewpoint. This article finds out that Chuncheon cluster embarked on cultural cluster strategies without concerning basic elements which had to be prepared from the initial stage. This problem worked as the serious obstacle hampering development of Chunchen cluster. This problem was the matter of policies in providing a direction of industry development as well as leading a local cultural cluster and led to a result of weakening the connection among cluster components. As a result, this article shows that the current status of Chuncheon cultural cluster is being entered the decline stage, and, therefore, suggests that the advanced policy to promote cluster for a next round is urgently needed. Stable closing the first round of cultural cluster policies and thorough preparation for the second round is the only practical solution to minimize side effects of cluster decline. It is the prerequisite to restore trust and, at the same time, reinforce relationship between members who consist of Chuncheon cultural cluster.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.288-291
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• 2008

본 논문은 실수 진화 알고리즘에 대해서 가상의 클러스터를 이용한 재조합 연산자 및 새로운 세대차 모델을 소개한다. 가상 클러스터의 자가 적응적인 크기 변화를 통해 자손의 생성범위를 적절히 조절하고, 선택과 대치를 포함한 진화방식을 개선하여 효율적인 세대차 크기를 구함으로서, 개체의 다양성 유지 및 탐색성능의 향상을 꾀하였다. 제안된 방법을 벤치마크 테스트 문제에 적용하여 G3 알고리즘과 CMA-ES 등과 성능을 비교하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.313-315
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• 2000

클러스터링에 있어서 K-means와 FCM(Fuzzy C-means)와 같은 기존의 알고리즘들은 지역적 최소 해에 수렴될 문제와 사전에 클러스터 개수를 결정해야 하는 문제점을 가지고 있다. 본 논문에서는 병렬 탐색을 통해 최적 해를 찾는 진화 알고리즘을 사용하여 지역적 최소 해에 수렴되는 문제점을 개선하였으며, 클러스터의 특성을 표준편차 벡터를 계산하여 중심으로부터 포함된 데이터가 얼마나 분포되어 있는지 알 수 있는 분산도와 임의의 데이터와 모든 중심들간의 거리의 비율로서 얻어지는 소속정도를 고려하여 클러스터간의 간격을 알 수 있는 분리도를 정의함으로써 자동으로 클러스터 개수를 결정할 수 있게 하였다. 실험데이터와 가우시안 분포에 의해 생성된 다차원 실험데이터를 사용하여 제안한 알고리즘이 이러한 문제점들을 해결하고 있음을 보인다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.25-27
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• 2001

진화 알고리즘에서 고려할 사항 중 하나는 문제와 관련 있는 진화연산 즉, 교배 연산과 돌연변이 연산을 정의하는 것이다. 일반적으로 교배 연산은 두 개체의 정보를 교환하는 재조합 연산으로써 진화의 속도를 촉진시키는 역할을 하고 돌연변이 인산은 개체집단의 다양성 을 유지시키는 역할을 한다. 그러나 이러한 진화연산자는 확률에 근거하여 모든 개체에 적용되는 맹목적인 연산이 가질 수 있는 진화시간 지연의 문제점을 갖는다. 본 논문에서는 맹목적 진화연산에 의한 진화 시간 지연을 해결하기 위해 휴리스틱 연산을 제안한다. 휴리스픽 연산은 문제의 특성에 맞지 않는 개체에만 적용되는 연산으로 진화 시간을 단축시킬 수 있다. 따라서 이러한 휴리스틱 연산의 타당성을 확인하기 위해 본 논문에서는 진화 알고리즘을 이용하여 최적의 클러스터 위치와 개수를 자동으로 찾아주는 문제에 클러스터의 특성을 고려한 휴리스틱 연산인 합병연산과 분할연산 그리고 K-means연산을 정의하여 다차원 실험데이터로 실험한 결과를 보이고 있다.

• Journal of the Korean association of regional geographers
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• v.21 no.3
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• pp.489-502
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• 2015

Sometimes called the Silicon Fen, the Cambridge cluster is well known as the most innovative hi-tech cluster in the world. This paper attempts to explore the evolutionary dynamics of the Cambridge cluster, drawing upon the triple-helix approach. To understand the evolutionary dynamics of the Cambridge cluster, it is important to examine the role and impact of the University of Cambridge, which is conceived as one of the best higher educational institutions in the world. The Cambridge cluster has been evolved on the basis of a strong university-industry relationship. University spin-outs have a long tradition in Cambridge with the history of more than a century, and they have played a critical role in making the cluster more innovative. Business networks and social capital molded for a long time in the region become a foundation for active local spin-out activities. However, since 2000 the central government started steadily to require British universities to be an entrepreneurial university, with the purpose of the reduction of university subsidies. As a result of this, spin-out activities in the Cambridge cluster show a sharp decline, which is a factor of threatening the dynamic evolution of the cluster.

• Journal of Korea Technology Innovation Society
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• v.21 no.4
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• pp.1207-1236
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• 2018

This paper explores the life cycle of innovation cluster, especially focussing on the Korean representative innovation cluster, Daedeok Innopolis. For this purpose, we review theoretically how an innovation cluster has been growing up. In particular, we discuss how a cluster has been formed and activated by governmental innovation policies from an evolutionary perspective. By doing so, the study identifies the typical features of an innovation cluster according to each dimensions of the cluster life cycle. The results of this study are as follows: First, in this study, Daedeok Innopolis has characteristics of latency, emergence, growth, and maturity from evolutionary perspective. Second, the governmental structure of the Daedeok Innopolis is a strong government-led and top-down structure, which has features of inclusiveness and flexibility such as umbrella policy. Third, the Daedeok Innopolis can be seen that adaptive or renewal development, as while, it can be applied fine adjustment the innovation cluster policy towards the recognition of innovation obstacle at each dimensions of the life cycle. Therefore, these discussions expose what kind of policy interventions should be addressed to form and develop the innovation cluster according to the cluster life cycle, as while, the development of adaptive policies during the risk and take-off period. Ultimately, the study provides that a different kind of policy instruments and tools should be implemented according to innovation cluster development and its distinctive characteristic per each dimensions of the cluster life cycle.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.78-80
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• 2000

클러스터링이란 주어진 데이터들을 유사한 성질을 가지는 군집으로 나누는 것으로 많은 분야에서 응용되고 있으며, 특히 최근 관심의 대상인 데이터 마이닝의 중요한 기술로서 활발히 응용되고 있다. 클러스터링에 있어서 기존의 알고리즘들은 지역적 최적해에 수렴하는 것과 사전에 클러스터 개수를 미리 결정해야 하는 문제점을 가지고 있다. 본 논문에서는 병렬 탐색을 통해 최적해를 찾는 진화알고리즘을 사용하여 지역적 최적해에 수렴되는 문제점을 개선하였으며, 자동으로 적절한 클러스터 개수를 결정할 수 있게 하였다. 또한 진화알고리즘의 단점인 탐색공간의 확대에 따른 탐색시간의 증가는 휴리스틱 연산을 정의하여 개선하였다. 제안한 알고리즘의 성능 및 타당성을 보이기 위해 가우시안 분포 데이터를 사용하여 제안한 알고리즘의 성능이 우수함을 보였다.

• Journal of the Korean association of regional geographers
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• v.20 no.3
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• pp.256-258
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• 2014

This paper aims at examining the role of university, industry and government, which constitute the triple-helix innovation system in hi-tech cluster, based on the case of the Research Triangle Park, North Carolina, USA. Firstly, the state government has played a catalytic role by building the horizontal governance of triple-helix actors. By doing so, the state government has made it possible for not only growing but also transforming the RTP as an high-tech cluster. Secondly, universities in the triangle area have played to some extent a limited role in sustaining the evolution of cluster. Thirdly, the RTP has long been dominated by a small group of large firms. However, the situation has been changed since 1990s, because new start-ups from universities and local large firms and the technology transfer activities between universities and firms have been increased in the RTP and its surrounding area. Finally, it argues that the continuous evolution of the RTP has been to some or large extent influenced by the transition from the exogenous development model to the endogenous development model.