• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.115-118
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• 2012

다양한 네트워크에서 군집을 분석하고 그 구조를 발견하는 것은 그 네트워크의 복잡도를 낮추어 전체 시스템을 이해하고 관리하는데 중요하다. 특히 기본적인 컴퓨팅이 가능한 여러 기기들이 자율적으로 서로 통신하여 군집을 이루는 자율 군집 네트워크에서 군집을 정확하게 발견하는 것은 집단행동 서비스를 실현하는데 있어서 중요한 기술이다. 따라서 본 연구에서는 자율 군집 네트워크에서 군집 탐지 기법을 제안한다. 제안하는 기법은 군집을 발견하고 그 군집을 식별하기 위해 해당 네트워크에서 한 노드를 공유하는 두 개의 간선 쌍에 대해 계층 군집화를 수행하고 계층 간에 간선 유사도를 계산하여 비교한다. 계층 군집화를 통한 간선들은 트리 구조로 표현할 수 있으며 최적의 분할 밀도를 이용하여 노드들을 클러스터링한 후 최종 군집으로 분리 한다.

• The Journal of Bigdata
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• v.5 no.1
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• pp.55-63
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• 2020

This study proposed an approach of clustering collective users' activities of location-based social networks using check-in data of Foursquare users in Seoul. In order to cluster the collective activities, we generated sequential rules of the activities using sequential rule mining, and then constructed activity networks based on the rules. We analyzed the activity networks to identify network structure and hub activities, and clustered the activities within the networks. Unlike previous studies that analyzed activity transition patterns of location-based social network users, this study focused on analyzing the structure and clusters of successive activities. Hubs and clusters of activities with the approach proposed in this study can be used for location-based services and marketing. They could also be used in the public sector, such as infection prevention and urban policies.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.63-81
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• 2023

This study endeavors to classify and categorize similar policy programs through network clustering analysis, using textual information from data-related policy programs in Korea. To achieve this, descriptions of data-related budgetary programs in South Korea in 2022 were collected, and keywords from the program contents were extracted. Subsequently, the similarity between each program was derived using TF-IDF, and policy program network was constructed accordingly. Following this, the structural characteristics of the network were analyzed, and similar policy programs were clustered and categorized through network clustering. Upon analyzing a total of 97 programs, 7 major clusters were identified, signifying that programs with analogous themes or objectives were categorized based on application area or services utilizing data. The findings of this research illuminate the current status of data-related policy programs in Korea, providing policy implications for a strategic approach to planning future national data strategies and programs, and contributing to the establishment of evidence-based policies.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.28-37
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• 2019

It is apparently clear that K-pop has been expanding its influence overseas, with its high growth rate. As a result, attempts have been made to analyze the characteristics of K-Pop in various academic fields. This research quantitatively used the participatory use process of K-Pop contents in voluntary participation and dissemination of the audience in the Trans-Media environment. The author examined the use of participatory K-Pop contents from the view point of reparability through big data content analysis. It has been revealed that K-Pop is spreading globally through social media, fans of various countries like to play K-Pop, and they make up their own content and form a participatory culture. In addition, we looked at when the moments of momentum in which participatory use is soaring were popular content and who was the publisher.

• Journal of Digital Contents Society
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• v.12 no.1
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• pp.69-75
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• 2011

A clustering process for nodes in a network is required to find communities from social networks. General clustering algorithm needs to be configured the number of communities in advance. The number of communities is a very important element because the result of clustering can be different, depending on it. In this paper, we define the external community relationship which is distinguished between communities. Using the external community relationship as an evaluation metric of clustering result, we propose a method to determine the number of communities dynamically. We compare the proposed method to existing methods based on the accuracy of the number of communities and the average purity of communities. Our results show favorable performance for these criteria compared to the existing methods that were evaluated.

• Journal of Digital Contents Society
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• v.14 no.1
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• pp.41-50
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• 2013

Social network analysis use the n nodes with l connections. About dozens or hundreds number of nodes are reasonable for social network analysis to the entire data. Beyond such number of nodes it will be difficult to analyze entire data. Therefore, it is necessary to separate the whole social networks, a method that can be used at this time is Clustering. You will be able to easily perform the analysis of the features of social networks and the relationships between nodes, if sub-group consists of all the nodes by Clustering. Clustering algorithm needs the interaction with the user and computer because it is need to pre-set the number of sub-groups. Sub-groups generated like this can not be guaranteed optimal results. In this paper, we propose dynamic sub-groups creating method using the external community association. We compared with previous studies by the number of sub-groups and sub-groups purity standards. Experimental results show the excellence of the proposed method.

• Journal of Wetlands Research
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• v.15 no.3
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• pp.397-406
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• 2013

Wetland conservation plan has been established to protect ecologically important wetlands based on vegetation integrity, spatial distribution of endangered species, but recently more demands are concentrated on the landscape ecological approaches such as topological relationship, neighboring area, spatial arrangements between wetlands at the broad scale. Landscape ecological analysis and graph theory are conducted to identify spatial characteristics related to core nodes and weak links of wetland networks in Nakdong basin. Regular planar model, which is selected for wetland networks, is applied in the Nakdong basin. The analysis indicates that 5 regional groups and 4 core wetlands are extracted with 15km threshold distance. The IIC and PC values based on the binary and probability models suggest that the wetland group C composed of main stream of Nakdong river and Geumho river is the most important area for wetland network. Wetland conservation plan, restoration projected of damaged and weak links between wetlands should be proposed through evaluating the node, links, and networks from wetlands at the local to the regional scale in Nakdong basin.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.432-436
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• 2010

Finding communities from network data including social networks can be done by clustering the nodes of the network as densely interconnected groups, where keeping interconnection between groups sparse. To exploit a clustering algorithm for community detection task, we need a well-defined similarity measure between network nodes. In this paper, we propose a new similarity measure named "Common Neighborhood Sub-graph density" and combine the similarity with affinity propagation, which is a recently devised clustering algorithm.

• Information and Communications Magazine
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• v.27 no.7
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• pp.36-41
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• 2010

본 논문에서는 개미 군집의 행동 생태를 모델로 하여 군집 시스템의 적응적 분업화, 전문화 특성을 살펴보고, 사물 통신 네트워크 분야로의 응용 가능성을 소개하고자 한다. 내 외적인 환경 변화에 대비하여 개미 군집이 어떻게 효율적인 관리와 전체 시스템의 운영 유지를 할 수 있는지는 시스템 관점의 분석 모델이 요구된다. 한 가능한 모델은 반응역(response threshold)과 일의 자극(task associated stimuli)의 관계로 적응적 반응함수를 사용하는 것이다. 본 논문에서는 적응적인 반응함수가 전체 군집의 효율성과 분업화 과정을 촉발시키는 형태로 발전하는 예제를 보여줄 것이다. 이러한 시스템 분석은 사물 통신 네트워크 분야 연구에 적용될 수 있고, 멀티 에이젼트 시스템에서 효율적인 정보 전송 및 유지, 노드 부하의 균등화, 통신 가능한 스웜 로봇의 업무 분업화 등 다양한 분야로 응용 가능성이 있음을 제안한다.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.2
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• pp.147-178
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• 2021

In this study, co-word analysis was conducted to investigate the intellectual structure of the domestic open access area. Through KCI and RISS, 124 research articles related to open access in Korea were selected for analysis, and a total of 1,157 keywords were extracted from the title and abstract. Network analysis was performed on the selected keywords. As a result, 3 domains and 20 clusters were extracted, and intellectual relations among keywords from open access area were visualized through PFnet. The centrality analysis of weighted networks was used to identify the core keywords in this area. Finally, 5 clusters from cluster analysis were displayed on a multidimensional scaling map, and the intellectual structure was proposed based on the correlation between keywords. The results of this study can visually identify and can be used as basic data for predicting the future direction of open access research in Korea.