• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.1-11
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• 2009

This study presents HCWS algorithm for researcher grouping on a weighted researcher network. The weights represent intensity of connections among researchers based on the number of co-authors and the number of co-authored research papers. To confirm the validity of the proposed technique, this study conducted an experimentation on about 80 research papers. As a consequence, it is proved that HCWS algorithm is able to bring about more realistic clustering compared with HCS algorithm which presents semantic relations among researchers in simple connections. In addition, it is found that HCWS algorithm can address the problems of existing HCS algorithm; researchers are disconnected since their connections are classified as weak even though they are strong, and vise versa. The technique described in this research paper can be applied to efficiently establish social networks of researchers considering relations such as collaboration histories among researchers or to create communities of researchers.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1084-1085
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• 2005

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.247-255
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• 2014

Safety management is becoming one of the most hot research issues in Korea. We collected the researcher information in two safety related journals of the journal of the Korea safety management & science and the journal of Korean society of safety. Social network analysis was used to analyse the research relationship among the authors in each journal. The degree centrality, the betweenness centrality, the closeness centrality and the proximity prestige centrality on each researcher were calculated to find out most influenced researcher. Biggest research groups in each journal were introduced by clustering technique.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.925-936
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• 2008

An association rule mining finds the relation among each items in massive volume database. In generating association rules, the researcher specifies the measurements randomly such as support, confidence and lift, and produces the rules. The rule is not produced if it is not suitable to the one any condition which is given value. For example, in case of a little small one than the value which a confidence value is specified but a support and lift's value is very high, this rule is meaningful rule. But association rule mining can not produce the meaningful rules in this case because it is not suitable to a given condition. Consequently, we creat insignificant error which is not selected to the meaningful rules. In this paper, we suggest clustering technique to association rule measures for finding effective association rules using measure of association.

• Korean Journal of Microbiology
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• v.39 no.2
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• pp.67-75
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• 2003

One large topic in comparative genomics is to predict functional annotation by classifying protein sequences. Computational approaches for function prediction include protein structure prediction, sequence alignment and domain prediction or binding site prediction. This paper is on another computational approach searching for sets of homologous sequences from sequence similarity graph. Methods based on similarity graph do not need previous knowledges about sequences, but largely depend on the researcher's subjective threshold settings. In this paper, we propose a genome sequence clustering method of iterative testing and graph decomposition, and a simple method to calculate a strict threshold having biochemical meaning. Proposed method was applied to known bacterial genome sequences and the result was shown with the BAG algorithm's. Result clusters are lacking some completeness, but the confidence level is very high and the method does not need user-defined thresholds.

• Journal of the Korean Society for information Management
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• v.40 no.2
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• pp.59-80
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• 2023

Traditional models for categorizing researcher types have mostly utilized research output metrics. This study proposes a new model that classifies researchers based on the characteristics of research collaboration. The model uses only research collaboration indicators and does not rely on citation data, taking into account that citation impact is related to collaborative research. The model categorizes researchers into four types based on their collaborative research pattern and scope: Sparse & Wide (SW) type, Dense & Wide (DW) type, Dense & Narrow (DN) type, Sparse & Narrow (SN) type. When applied to the quantum metrology field, the proposed model was statistically verified to show differences in citation indicators and co-author network indicators according to the classified researcher types. The proposed researcher type classification model does not require citation information. Therefore, it is expected to be widely used in research management policies and research support services.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.356-359
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• 1997

Recently many studies have been conducted of fuzzy modeling since it can describe a nonlinear system better than the conventional methods. A famous researcher, M. Sugeno, suggested a fuzzy model which superbly describes a nonlinear system. In this paper, we suggest a new identification method for Sugeno-typo fuzzy model. The suggested algorithm is much simpler than the original identification strategy adopted in [1]. The algorithm suggested in this paper is somewhat similar to that of [2]. that is, the algorithm suggested in this paper consists of two consists of two steps: coarse tuning and fine tuning. In this paper, double clustering strategy is proposed for coarse tuning. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.13-32
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• 2006

Biologists are attempting to group genes based on the temporal pattern of gene expression levels. So far, a number of methods have been proposed for clustering microarray data. However, the results of clustering depends on the genes selection, therefore the gene selection with significant expression difference is also very important to cluster for microarray data. Thus, this paper present the results of broad comparative studies to time course microarray data by considering methods of gene selection, clustering and cluster validation.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.39-45
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• 2010

As the value of environment is increasing, the water quality has been a matter of interest to the nation and people. Research on water quality has been widely studied, but focused on geographical characteristic and river characteristics like inflow, outflow, quantity and speed of water. In this paper, two approaches to measure the similarity of sampling sites by using water quality data are discussed and compared with two-years empirical data of Yongdam-Dam. The existing method has calculated their similarities with principal component scores. The proposed approach in this paper use correlation matrix of water quality related variables and MDS for measuring the similarity, which is shown to be better in the sense of being clustering which is identical to geographical clustering since it can consider the time series pattern of water quality.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.230-233
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• 2011

Chronic disease is linked to patient's' lifestyle. Therefore, doctor has to monitor his/her patient over time. This may involve reviewing many reports, finding any changes, and modifying several treatments. One solution to optimize the burden is using a visualizing tool over time such as a timeline-based visualization tool where all reports and medicine are integrated in a problem centric and time-based style to enable the doctor to predict and adjust the treatment plan. This solution was proposed by Bui et. al. [2] to observe the medical history of a patient. However, there was limitation of studying the diabetes patient's history to find out what was the cause of the current development in patient's condition; moreover what would be the prediction of current implication in one of the diabetes' related factors (such as fat, cholesterol, or potassium). In this paper, we propose a Grid-based Interactive Diabetes System (GIDS) to support bioinformatics analysis application for diabetes diseases. GIDS used an agglomerative clustering algorithm as clustering correlation algorithm as primary algorithm to focus medical researcher in the findings to predict the implication of the undertaken diabetes patient. The algorithm was Chronological Clustering proposed by P. Legendre [11] [12].