• Journal of the Korean Society for information Management
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• v.27 no.2
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• pp.241-252
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• 2010

Titles have been regarded as having effective clustering features, but they sometimes fail to represent the topic of a document and result in poorly generated document clusters. This study aims to improve the performance of document clustering with titles by suggesting titles in the citation bibliography as a clustering feature. Titles of original literature, titles in the citation bibliography, and an aggregation of both titles were adapted to measure the performance of clustering. Each feature was combined with three hierarchical clustering methods, within group average linkage, complete linkage, and Ward's method in the clustering experiment. The best practice case of this experiment was clustering document with features from both titles by within-groups average method.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.705-712
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• 2013

Active learning aims to improve the performance of a classification model by repeating the process to select the most helpful unlabeled data and include it to the training set through labelling by expert. In this paper, we propose a method for active learning based on hierarchical agglomerative clustering using Ward's linkage. The proposed method is able to construct a training set actively so as to include at least one sample from each cluster and also to reflect the total data distribution by expanding the existing training set. While most of existing active learning methods assume that an initial training set is given, the proposed method is applicable in both cases when an initial training data is given or not given. Experimental results show the superiority of the proposed method.

• Journal of Korea Port Economic Association
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• v.34 no.3
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• pp.17-52
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• 2018

The purpose of this paper is to measure the clustering change and analyze empirical results. Additionally, by using k-means, hierarchical, and mixed models on Asian container ports over the period 2006-2015, the study aims to form a cluster comprising Busan, Incheon, and Gwangyang ports. The models consider the number of cranes, depth, birth length, and total area as inputs and container twenty-foot equivalent units(TEU) as output. Following are the main empirical results. First, ranking order according to the increasing ratio during the 10 years analysis shows that the value for average linkage(AL), mixed ward, rule of thumb(RT)& elbow, ward, and mixed AL are 42.04% up, 35.01% up, 30.47%up, and 23.65% up, respectively. Second, according to the RT and elbow models, the three Korean ports can be clustered with Asian ports in the following manner: Busan Port(Hong Kong, Guangzhou, Qingdao, and Singapore), Incheon Port(Tokyo, Nagoya, Osaka, Manila, and Bangkok), and Gwangyang Port(Gungzhou, Ningbo, Qingdao, and Kasiung). Third, optimal clustering numbers are as follows: AL(6), Mixed Ward(5), RT&elbow(4), Ward(5), and Mixed AL(6). Fourth, empirical clustering results match with those of questionnaire-Busan Port(80%), Incheon Port(17%), and Gwangyang Port(50%). The policy implication is that related parties of Korean seaports should introduce port improvement plans like the benchmarking of clustered seaports.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.3
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• pp.79-99
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• 2020

The purpose of this study is to investigate the model of cluster analysis techniques for grouping public libraries and analyze their characteristics. Statistical data of public libraries of the National Library Statistics System were used, and three models of cluster analysis were applied. As a result of the study, cluster analysis was conducted based on the size of public libraries, and it was largely divided into two clusters. The size of the cluster was largely skewed to one side. For grouping based on size, the ward method of hierarchical cluster analysis and the k-means cluster analysis model were suitable. Three suggestions were presented as implications of the grouping method of public libraries. First, it is necessary to collect library service-related data in addition to statistical data. Second, an analysis model suitable for the data set to be analyzed must be applied. Third, it is necessary to study the possibility of using cluster analysis techniques in various fields other than library grouping.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.3
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• pp.5-27
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• 2020

This study aimed at analyzing the keywords and authors of the KCI listed theological journals and finding the official research performance of Korean theology. This study divided the periods in two according to how duplicate the authors are and found hierarchical clusters by analyzing 92 keywords using the McQuitty method. In analyzing them, the Ward linkage method was selected to prevent the authors from gathering into a small number of clusters. Also, to find how influential the journals were to the keywords, the keywords and the percentage of the journals in them were presented together. The authors were analyzed in terms of deciding the positions of them using normalized performance index representing the number of journals and growth index as a growth tendency. Especially, significant researchers were all reformed theologians in a growth index. In the analysis of the keywords of the KCI journals and the authors, the main subject terms of the Korean theology were related to systematic theology and the New Testament. By analyzing the KCI listed journals as the Korean official citation index, this study has made a difference to the advanced articles analyzing the non-KCI listed theological journals.