• The Journal of Korean Association of Computer Education
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• v.23 no.1
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• pp.77-90
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• 2020

The purpose of the study is to provide educational implications for more effective Problem-based learning(PBL) by investigating students' learning types based on their online learning behaviors. A total of 1,341 students participated in the study, and they engaged in a six-week-long PBL program run by K University. For the study, participants' online activity data were collected. From the data, a total of 48 variables that represent their various online learning behaviors were extracted. Based on the variables, hierarchical cluster analysis was conducted to analyze learning types. Also, the differences in learning characteristics and achievements were investigated by considering types of learning. As a result, the learning types in online PBL were classified as 'high-level participation (cluster 1)', 'medium-level participation (cluster 2)', and 'low-level participation (cluster 3)'. In addition, the achievement level was found to be highest in 'high-level participation (cluster 1)' and lowest in 'low-level participation (cluster 3)'. Based on the results, the implications for improving online PBL were suggested.

• Journal of The Korean Association For Science Education
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• v.42 no.6
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• pp.611-619
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• 2022

The purpose of this study is to explore the possibility of applying big data analysis to provide appropriate feedback to students using evaluation data in science education at a time when interest in educational data mining has recently increased in education. In this study, we use the evaluation data of 2,576 students who took 24 questions of the national assessment of educational achievement. And we use K-means cluster analysis as a method of unsupervised machine learning for clustering. As a result of clustering, students were divided into six clusters. The middle-ranking students are divided into various clusters when compared to upper or lower ranks. According to the results of the cluster analysis, the most important factor influencing clusterization is academic achievement, and each cluster shows different characteristics in terms of content domains, subject competencies, and affective characteristics. Learning motivation is important among the affective domains in the lower-ranking achievement cluster, and scientific inquiry and problem-solving competency, as well as scientific communication competency have a major influence in terms of subject competencies. In the content domain, achievement of motion and energy and matter are important factors to distinguish the characteristics of the cluster. As a result, we can provide students with customized feedback for learning based on the characteristics of each cluster. We discuss implications of these results for science education, such as the possibility of using this study results, balanced learning by content domains, enhancement of subject competency, and improvement of scientific attitude.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.2 no.4
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• pp.19-41
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• 2007

This research has a goal which is suggesting the way of constructing 'Cluster' which mean scheming the commencement of an enterprise in an early stage. Now it is reorganized into a IT industry structure 'Time-to market growth' is burst as a big issue. in that point, this research analyze the core success factor which is drawing from the existing IT industrial complex, and then it will be used to draw up to the 'Idealistic growth-support Cluster' on the basis of it, we pulled out various issues about the Corporate in the early stage of its growth. Therefore, this research is focused on presenting the ideal network(net) by considering the Network that organizations and business in Cluster or the network including the factors linked organizations and business in Cluster. therefore, this research carried out three big analysis. from the case investigation we pulled out the core growth factor, and then we approached the analysis of net structure for making application to Network Analysis. and then we analyzed that the characteristics of the Network after measuring by on the basis of analyzing core growth factor. and especailly, this research carried out the Core analysis for recognition of Core- support-frame by base Centrality Test on the net which is composed of growth support organizations at each Business. Judging from this, we can help to make full use of resources for the network analysis in Cluster and establish the Network Strategy by Structure comparison between the structure of industry-Cluster and ideal Business-support networks on the basis of the analysis from the Core-success-factor

• Journal of Korean Ophthalmic Optics Society
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• v.20 no.4
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• pp.405-414
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• 2015

Purpose: We analyse characteristics of optical shop customer's segmented market by using clustering analysis, and we expect it would be a useful indicator of marketing strategy for optical shops. Methods: Survey was conducted from March 10 to March 31, 2015. The survey asked customers who have visited optical shops in Seoul and Northern Gyeonggi-do regions, and analyzed by utilizing SPSS v.10.0 statistical package program. The analysing methods are frequency analysis, factor analysis about variable of values, clustering analysis for market segmentation, and crosstabs. Results: The market is segmented based on values. In the process of establishing marketing strategy, it is useful to establish strategy by classifying customers into 3 types of cluster; "middle level value oriented cluster", "high level value oriented cluster", "high level value oriented and non-religious cluster". In marketing strategy of progressive lenses, it turned out that the most important strategy is to target self-employed person in "middle level value oriented cluster". Conclusions: As a result of market segmentation by using clustering analysis, it was classified into 3 types of cluster, and we found that most important customer for progressive lenses is self-employed person in "middle level value oriented cluster" who is more than 41 years old.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.387-394
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• 2007

This paper considers several stratification strategies for multivariate and multipurpose survey with several quantitative stratification variables. We propose three methods of stratification based on, respectively, the method of cumulative frequency square root which is the most popular one in univariate stratification, cluster analysis, and factor analysis followed by cluster analysis. We then compare the efficiency of those methods using the Dong-Eup-Myun data of the holding numbers of farming machines, extracted from the 2001 Agricultural Census. It turned out that the method based on cluster analysis with factor analysis would be a relatively satisfactory strategy.

• Journal of Soil and Groundwater Environment
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• v.7 no.3
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• pp.19-32
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• 2002

This study is objected to identify the relations between surface- and shallow ground-water and the seasonal variation of their qualities in watersheds near Muju area. The water type shows mainly Ca-$HCO_3$type. Heavy-metal contamination of surface water is locally detected, due to the mixing with mine drainage. In October nitrate concentration is especially high in densely populated area. Cluster Analysis and Principal Component Analysis are implemented to interpret the complexity of the chemical variation of surface- and ground-water with large amount of chemical data. Based on the cluster analysis, surface-water was divided into five groups and ground-water into three groups. Principal Component Analysis efficiently supports the result of cluster analysis, allowing the identification of three main factors controlling the water quality. There are (1) hydrogeochemical factor, (2) anthropogenic factor and (3) heavy metal contaminated by mine drainage.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.308-314
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• 2022

This study deals with the technology Convergence Analysis by IPC Code-Based Social Network Analysis of Healthcare Patents filed in Korea. The relationship between core technologies is visualized using Social Network Analysis. At the subclass level of healthcare patents, 1,155 cases (49.4%) of patents with complex IPC codes were investigated, and as a result of Social Network Analysis on them, the IPC codes with the highest Degree Centrality were A61B, G16H, and G06Q, in that order. The IPC codes with the highest Betweenness Centrality are in the order of A61B, G16H, and G06Q. In addition, it was confirmed that healthcare patents consist of two large technology clusters. Cluster-1 corresponds to related business models centered on A61B, G16H and G06Q, and Cluster-2 is consisting of H04L, H04W and H04B. The technology convergence core pairs of the healthcare patent is [G16H-A61B] and [G16H-G06Q] in Cluster-1, and [H04L-H04W] in Cluster-2. The results of this study can contribute to the development of core technologies for healthcare patents.

• Cartoon and Animation Studies
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• s.36
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• pp.593-617
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• 2014

This article is aimed at evaluating the comic industries cluster in the cartoonists' perspective in terms of benefits, innovation milieu and loyalty. This article surveyed the 105 cartoonists in the Bucheon comic industries cluster, which has been established since 1998. As a result of analysis, cartoonists evaluated the comic industries cluster in term of facilities, knowledge and information, and social relationship in the positive way. However, the business network with the comic companies, the other contents industries is not established. The communication and collaboration between the cartoonists and local communities is not active in the Bucheon comic industries cluster. In addition, while comic industries cluster is effective in terms of city branding, the comic industries cluster is not effective in terms of economic impacts. In general, cartoonists' loyalty to the comic industries cluster is highly evaluated. The five factors such as knowledge, policy, urban regeneration, facilities are very significant in terms of the cartoonists' loyalty. This article concludes with a discussion of the sustainable strategies of the comic industries cluster in the context of creative city through comic resources.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.1
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• pp.15-26
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• 2002

The purpose of this study was to classier the somatotype of late middle-aged women and to analyze the characteristics of each somatotype. The subjects were 337 late middle-aged women and their age range os from 45 to 59 fears old. Data were collected through anthropometry and photometry and analyzed by factor analysis, cluster analysis and discriminant analysis. The results were as follows; 1. The result of factor analysis indicated that 9 factors were extracted through factor analysis and those factors comprised 83.56 percent of total valiance. 2. Using factor scores, cluster analysis was carried out and the subject were classified into 4 cluster. Each cluster was classified as their body front and side view contour. Type 1 is tall, slim, and lower balk is flat on the side. Type 2 is standard and lean-back type on the side. Type 3 is standard height and weight, H type in front, and belly-protruded on the side. Type 4 is short, fat, and the side is hip-protruded. 3. According to the stepwise discriminant analysis, the 9 important items in classifying the somatotype of the late middle-aged women are as follows ; lower back tilt angle, hip depth(back) -back waist depth(back), bust depth(fore) - anterior waist depth(fore), jugular fossa point(fore), upper back tilt angle, burst breadth -waist breadth, right shoulder tilt, height of shoulder - height of anterior waist, abdomen breath. The correct classification rate for these items is as exact as 84.62%.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.19-24
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• 2021

In this paper, we study performance factor of machine learning in the distributed environment using Apache Spark and presents an efficient distributed processing method through experiments. This work firstly presents performance factor when performing machine learning in a distributed cluster by classifying cluster performance, data size, and configuration of spark engine. In addition, performance study of regression analysis using Spark MLlib running on the Hadoop cluster is performed while changing the configuration of the node and the Spark Executor. As a result of the experiment, it was confirmed that the effective number of executors was affected by the number of data blocks, but depending on the cluster size, the maximum and minimum values were limited by the number of cores and the number of worker nodes, respectively.