• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2587-2605
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• 2022

In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity. However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs. To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA. Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed. Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified. Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities. Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization. Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.

• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.715-723
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• 2002

This paper is to show a clustering application of a density estimation method that utilizes the Gaussian mixture model. We define "closeness measure" as a clustering criterion to see how close given two Gaussian components are. Closeness measure is defined as the ratio of log likelihood between two Gaussian components. According to simulations using artificial data, the clustering algorithm turned out to be very powerful in that it can correctly determine clusters in complex situations, and very flexible in that it can produce different sizes of clusters based on different threshold valuesold values

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권3호
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• pp.165-170
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• 2011

Due to advances in hardware performance, user-friendly interfaces are becoming one of the major concerns in information systems. Linguistic conversation is a very natural way of human communications. Fuzzy techniques have been employed to liaison the discrepancy between the qualitative linguistic terms and quantitative computerized data. This paper deals with linguistic queries using clustering results on data sets, which are intended to retrieve the close clusters or distant clusters from the clustering results. In order to support such queries, a fuzzy technique-based method is proposed. The method introduces distance membership functions, namely, close and distant membership functions which transform the metric distance between two objects into the degree of closeness or farness, respectively. In order to measure the degree of closeness or farness between two clusters, both cluster closeness measure and cluster farness measure which incorporate distance membership function and cluster memberships are considered. For the flexibility of clustering, fuzzy clusters are assumed to be formed. This allows us to linguistically query close or distant clusters by constructing fuzzy relation based on the measures.

• 아동학회지
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• 제28권4호
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• pp.19-33
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• 2007

This study examined the effects of closeness of peer relationships on intersubjectivity in young children's joint story making of wordless picture books. The joint story making activity of 32 five-year-old children was videotaped and transcribed, and the structure and negotiation types of interaction were examined by Goncu's (1993a) measure of intersubjectivity. Results showed (1) closeness of peer relationship was related to the structure of intersubjectivity children working with very close peers exhibited more turns. (2) Children working with very close peers used more extension and acceptance negotiation types, whereas the children working with non-close peers used more building-on of own ideas and irrelevant acts of negotiation.

• Communications for Statistical Applications and Methods
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• 제29권4호
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• pp.487-498
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• 2022

Social network analysis (SNA) techniques have recently been developed to monitor and detect abnormal behaviors in social networks. As a useful tool for process monitoring, control charts are also useful for network monitoring. In this paper, the degree and closeness centrality measures, in which each has global and local perspectives, respectively, are applied to an exponentially weighted moving average (EWMA) chart and a multinomial cumulative sum (CUSUM) chart for monitoring undirected weighted networks. In general, EWMA charts monitor only one variable in a single chart, whereas multinomial CUSUM charts can monitor a categorical variable, in which several variables are transformed through classification rules, in a single chart. To monitor both degree centrality and closeness centrality simultaneously, we categorize them based on the average of each measure and then apply to the multinomial CUSUM chart. In this case, the global and local attributes of the network can be monitored simultaneously with a single chart. We also evaluate the performance of the proposed procedure through a simulation study.

• 한국철도학회논문집
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• 제20권3호
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• pp.413-422
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• 2017

본 연구에서는 네트워크 중심성 지표를 사용하여 지하철 네트워크의 개별 노드의 중요성을 분석하고 이로부터 한국 지하철 네트워크의 특성을 분석하였다. 중심성 측도로 매개, 근접 그리고 차수 중심성을 사용하였다. 본 연구에서는 기존에 제안된 매개 중심성 지표와 승객들의 실제 흐름양을 함께 고려한 가중 매개 중심성 지표를 새롭게 제안하였다. 그리고 본 연구에서 제안한 여러 중심성 지표들 사이의 상관관계를 조사함으로서 서울 수도권 지하철과 승객 흐름의 구조적 특성 등을 조사하였다. 아울러 승객들 흐름의 편중 현상을 조사하기 위하여 멱분포(Power-law) 분석을 수행하여 결과 분석의 신빙성을 더하였다.

• ETRI Journal
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• 제38권3호
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• pp.540-550
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• 2016

Spectral clustering is a powerful tool for exploratory data analysis. Many existing spectral clustering algorithms typically measure the similarity by using a Gaussian kernel function or an undirected k-nearest neighbor (kNN) graph, which cannot reveal the real clusters when the data are not well separated. In this paper, to improve the spectral clustering, we consider a robust similarity measure based on the shared nearest neighbors in a directed kNN graph. We propose two novel algorithms for spectral clustering: one based on the number of shared nearest neighbors, and one based on their closeness. The proposed algorithms are able to explore the underlying similarity relationships between data points, and are robust to datasets that are not well separated. Moreover, the proposed algorithms have only one parameter, k. We evaluated the proposed algorithms using synthetic and real-world datasets. The experimental results demonstrate that the proposed algorithms not only achieve a good level of performance, they also outperform the traditional spectral clustering algorithms.

• 한국생활과학회지
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• 제22권3호
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• pp.391-404
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• 2013

This study examined the moderating role of teacher-child relationship on the relation between children's problem behavior and peer victimization. Participants were 198 children(97 boys, 101 girls; recruited from classes with 5-6 year olds) and their kindergarten teachers. The teachers completed the rating scales to measure the children's peer victimization, problem behavior and teacher-child relationship. The collected data were analyzed using descriptive statistics, t-tests, correlations, and hierarchical multiple regressions. Boys and girls were analyzed separately. Results showed that children's problem behavior had positive relation to their peer victimization. Teacher-child relationship significantly related to children's peer victimization. Hierarchical regression analysis indicated that the interaction of boys' withdrawal behavior and teacher-child closeness predicted boy's peer victimization. Boys' withdrawal behavior, whose teachers demonstrated the lowest level of teacher-child closeness, associated significantly with their peer victimization. Boys' withdrawal and aggressive behavior had significant relation to their peer victimization, especially for the highest level of teacher-child conflictual relationship. Findings suggested the importance of teacher-child relationship in the context of intervention planning for peer victimization.

• 정보관리학회지
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• 제33권2호
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• pp.61-76
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• 2016

본 연구는 명망성 지표(closeness prestige, input domain, proximity prestige)를 인용 네트워크 내에서 특정 노드의 영향력 측정을 위한 유용한 척도로 제안하는 것을 목적으로 한다. 명망성 지표의 영향력 측정기준에 대해 알려진 바가 없으므로 본 연구는 이 세 개의 명망성 지표와 다른 영향력 지표를 비교하고자 한다. 무방향 네트워크의 중심성 지표와 유사하게 명망성 지표는 유방향 네트워크에서 특정 중심 노드들을 차별화 시켜준다. 저자 인용 네트워크에서 수행된 상관분석과 주성분분석을 통하여 본 연구는 기존 영향력 지표와 차별된 명망성 지표만의 측정기준을 발굴하였다. 세 개의 무방향 네트워크 중심성 지표와 더불어 단순인용수, h-index, PageRank를 본 연구에서 제시한 명망성 지표와 비교하였다. 이러한 명망성 지표는 기존 영향력 지표와는 차별화된 영향력을 측정하고 있다는 결과를 도출하였으며 명망성 지표는 간접적인 영향력을 기존의 다른 영향력 지표는 직접적인 영향력을 반영한다.

• 한국임상수의학회지
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• 제27권4호
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• pp.427-434
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• 2010

Correlation is a technique used to measure the strength or the degree of closeness of the linear association between two quantitative variables. Common misuses of this technique are highlighted. Linear regression is a technique used to identify a relationship between two continuous variables in mathematical equations, which could be used for comparison or estimation purposes. Specifically, regression analysis can provide answers for questions such as how much does one variable change for a given change in the other, how accurately can the value of one variable be predicted from the knowledge of the other. Regression does not give any indication of how good the association is while correlation provides a measure of how well a least-squares regression line fits the given set of data. The better the correlation, the closer the data points are to the regression line. In this tutorial article, the process of obtaining a linear regression relationship for a given set of bivariate data was described. The least square method to obtain the line which minimizes the total error between the data points and the regression line was employed and illustrated. The coefficient of determination, the ratio of the explained variation of the values of the independent variable to total variation, was described. Finally, the process of calculating confidence and prediction interval was reviewed and demonstrated.