• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.87-91
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• 2011

강수는 지역에 따라 발생양상이 매우 다른 자연현상 중 하나이다. 이러한 강수를 효과적으로 분석하여 확률강수량을 산정하기위해서 수문학에서는 다양한 방법이 시도되어 왔다. 우리나라에서는 지점빈도해석을 통한 확률강수량을 주로 사용해왔으나 최근 들어 Hosking and Wallis(1997)가 제안한 지역빈도해석을 활용을 적극 도모 하고 있는 중이다. 이러한 지역빈도해석 기법은 지점빈도해석 기법에 비하여 한정된 강수자료를 활용하는 측면 등 여러 가지 장점을 가진 확률 강수량 산정방법이다. 그러나 이 기법을 적용하여 확률강수량을 산정하기 위해서는 강수의 지역구분을 먼저 수행하여야 한다. 강수지역의 구분을 위해서는 여러 가지 기법이 존재하나 최근에는 Cluster 기법 중 K-means 방법이나 Fuzzy c-means 방법 등을 주로 적용하여 지역구분을 수행하고 있다. 그러나 K-means 방법이나 Fuzzy c-means 방법 등은 산정 방법내에서 최적 군집수를 결정할 수 있는 알고리즘이 없기 때문에 임의적으로 최적 군집수를 결정하여야 한다. 본 연구에서는 이러한 단점을 극복하기 위하여 Cluster 평가지수 중 하나인 Dunn 지수를 이용하여 최적 군집수를 제시하고자 한다. 본 연구에서 강수지역을 구분하기 위하여 적용한 인자는 월 평균 강수량, 연 평균 강수량, 월 최대 강수량, 경도, 위도, 고도 등이며, 이를 K-means, PAM 및 친근도 전파 기법을 통하여 강수지역을 구분하였다. 적정 군집수를 임의적으로 증가시켜 가면서 Dunn 지수를 산정하였다. 산정된 결과를 통하여 최적 군집수를 결정하였다.

• Journal of Korean Society of Rural Planning
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• v.26 no.1
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• pp.101-112
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• 2020

Recently, public awareness regarding mountain villages' landscapes is increasing. Thus, this study aimed to provide standards for conservation, management and creation of mountain village landscape by characterizing and classifying those exist. 286 mountain villages' data were collected and 19 variables - extracted from GIS spatial information and statistic data of mountain villages, chosen as right sources according to former studies - were utilized to conduct factor and cluster analysis. As a result of the factor analysis, 7 characteristics of the mountain villages' landscapes were defined - 'Location', 'Cultivation', 'Ecology·Nature', 'Tourism', 'Residence', 'Recreation'. The K-means cluster analysis categorized the mountain villages' landscapes into four types - 'Residential', 'Touristic', 'General', 'Environmentally protected'. The classification was examined to be appropriate by field assessment, and basic guidelines of mountain village landscape management were set. The results of this study are expected to be utilized planning and implementing regarding mountain village landscape in the future.

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.7-15
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• 2011

Recently, airport competitiveness measure is not only passenger and cargo throughput but also value-added activities of their hinterland and airport city. That is, airport competitiveness comes from airport versus airport to airport with their own-supplied city and hinterland connected with airport to provide diversified functions. This study surveyed and analyzed how to form a cluster focused on Incheon International Airport and what are important factors to form of cluster in achieving competences. These clusters need government's political support. In this case, there was a shortage of specialized human resources in competent local suppliers, and limited informations sharing.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.3
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• pp.585-599
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• 2014

Zusammenfassung : Seit den 1990er Jahren hat Muenchen sich zu einem der fuehrenden europaeischen Biotechnologie Clustern entwickelt. Cluster, d.h. raeumliche Konzentrationen von miteinander vernetzten Unternehmen und Institutionen entlang einer Wertschoepfungskette, stellt fuer Unternehmen und Regionen einen erheblichen Wettbewerbsvorteil dar. Ziel dieser Beitrag ist es, die Herausbildung und Entwicklung des Innovationsclusters in Deutschland, am Beispiel von Biotech Cluster Muenchen zu analysieren. Im Folgenden wird zunaechst der Versuch unternommen, eine uebergreifende Kozeption regionaler Unternehmensclustern theoretisch zu eroerten. Darauf aufbauend wird anhand des Biotech Clusters Muenchen die Struktur und Entwicklung des Innovationsclusters empirisch aufgezeigt.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.241-248
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• 2018

A Recommender System (RS) is a system that provides optimized information to users in an over-supply situation. The key to RS is to accurately predict the behavior of the user. The Matrix Factorization (MF) method was used for this prediction in the early stage, and according to the recent SNS development, social information is additionally utilized to improve prediction accuracy. In this paper, we use RS internal trust cluster, which was overlooked in previous studies, to further improve performance and analyze the characteristics of trust clusters.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.345-354
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• 2016

Inhomogeneous Poisson process models are widely applied to landslide data to understand how environmental variables systematically influence the risk of landslides. However, those models cannot successfully explain the clustering phenomenon of landslide locations. In order to overcome this limitation, we propose to use a Cauchy cluster process model and show how it improves the goodness of fit to the landslide data in terms of K-function. In addition, a numerical study is performed to select the optimal estimation method for the Cauchy cluster process.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.6-6
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• 2015

본 연구에서는 일단위로 제공되는 RCP 시나리오를 Poisson Cluster 기법을 활용하여 시간강우량으로 생성할 수 있는 모형을 개발하는데 목적이 있다. 일반적으로 시간단위 강우량의 경우 수자원 설계 또는 강우-유출 분석시 가장 기본이 되는 입력 자료로서 이에 대한 모의기법 확립이 기후변화에 따른 수문학적 영향 검토의 신뢰성을 결정짓는 핵심 요소이다. 그러나 국내 다수 연구를 살펴보면 기후변화 시나리오의 시 공간적 상세화 기법을 활용한 일단위 상세화 연구는 다수 존재하였지만, 일단이 이하의 시간적 규모에 대한 연구는 미진한 실정이다. 이러한 이유로 본 연구에서는 시단위 상세화 기법시 일반적으로 사용되고 있는 Poisson Cluster 기법을 활용하여 국내 실정에 맞는 시단위 상세화 기법을 개발고자 한다. 본 연구에서는 RCP 시나리오를 시단위강우량 자료로 생성하기 위해 다음과 같은 연구를 진행하였다. 첫째, 본 연구에서는 기상청에서 제공하는 RCP($27km{\times}27km$) 시나리오를 활용하였으며, 1km 격자 단위로 시공간적 상세화 기법을 수행하였다. 둘째, 시공간적으로 상세화 된 자료를 Poisson Cluster 기법을 기반으로 시간단위 자료를 생성하였으며, 기본적인 통계치(평균, 분산, 왜곡도 등)를 활용하여 관측값과 비교 분석 하였다. 마지막으로, 미래 기후변화 시나리오를 동일한 방법으로 시간단위 자료를 생성하고 연 최대값을 추출하여 빈도해석을 통해 미래 극치 확률강우량을 평가하였다. 본 연구 결과 시간단위 자료를 제공함으로써 미래 수자원 설계 및 영향평가를 효과적으로 수행할 것으로 기대되며, 수문기상변화 예측을 위한 신뢰성 있는 자료로 활용될 수 있을 것으로 판단된다.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.75-86
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• 2007

The subjects were fifteen dong at downtown of Busan. LQ index and cluster analysis were used to research space functions of downtown in both years, that is to say, 2000 and 2005. At the analysis of LQ index in 2000, the secondary industries were specialized at traditional markets as well as large-scaled commercial districts, while the tertiary industries were done at financial business districts. LQ index in 2005 did not make change mostly comparing with the one in 2000: But, main businesses at downtown that belonged to the tertiary industries rapidly dwindled at old downtown to make appearance at Seomyeon of new downtown. Main businesses at old downtown in the past moved to new downtown to dwindle main functions at old downtown. The cluster analysis was done by using LQ index to classify into three clusters. The first cluster was old downtown that functions of downtown dwindle, and the second cluster was residence area, and the third cluster was the area where space function at downtown was very much active. The findings were as follow: Firstly, various kinds of urban regeneration plans should be made to control dwindling of old downtown and to establish comprehensive and systematic plans on new downtown. Secondly, downtown space functions could be placed depending upon specialization of each industry so that industries being suitable to the area should be introduced to construct development base.

• The Journal of Korean Association of Computer Education
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• v.7 no.1
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• pp.151-165
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• 2004

The purpose of the study was to explore the potential of the Cluster Analysis and the Community Analysis of Social Network Analyses family in job-task analysis for curriculum design. These two multivariate analysis techniques were expected to bring us relevant and scientific information as well as inspiration in investigating the structure and nature of job system, which are critical in developing relevant curriculum. To pursue the purpose mentioned above, qualitative and quantitative data were collected from "S" Corporate, a major large high-tech manufacturing company, and analyzed by relevant analytic procedures. Results indicate that there are discrepancies between formal job structures and actual ones. Following Community analysis showed that the presence of center-marginal structure along with clustering structure in the current job formation. Interpretations of the results of the study are provided in light of past research and additional data collected from the study. Implications of the study are also discussed along with suggestions for future research.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1609-1622
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• 2009

In wireless sensor networks, a clustering protocol is an efficient method to prolong network lifetime. In general, it results in more energy consumption at the cluster-head node. Hence, such a protocol must changes the cluster formation and cluster-head node in each round to prolong the network lifetime. But, this method also causes large amount of energy consumption during the set-up process of cluster formation. In order to improve energy efficiency, in this paper, we propose a new clustering algorithm. In this algorithm, we exclude duplicated data of adjacent nodes and transmits the threshold value. We define a group as the sensor nodes within close proximity of each other. In a group, a node senses and transmits data at a time on the round-robin basis. In a view of whole network, group is treated as one node. During the setup phase of a round, intra clusters are formed first and then they are re-clustered(network cluster) by choosing cluster-heads(group). In the group with a cluster-head, every member node plays the role of cluster-head on the round-robin basis. Hence, we can lengthen periodic round by a factor of group size. As a result of analysis and comparison, our scheme reduces energy consumption of nodes, and improve the efficiency of communications in sensor networks compared with current clustering methods.