• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.107-112
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• 2022

Nowadays clusters are recognized as an important instrument for promoting industrial development, innovation, competitiveness and growth. An educational cluster is a set of interrelated vocational educational institutions of various levels that are united by industry with each other and are connected by partnership with industry enterprises. This article attempts to develop and describe cluster model of university students' multilingual training. The purpose of this study is to describe multilingual training of university students and their polycultural competencies formation and to define the process of multilingual training in form of a cluster. The authors consider clusters as an integral part of the educational campus within the concept framework of Shadrinsk State Pedagogical University. To determine the essence of the concept of a cluster model of university students' multilingual training, theoretical, empirical, observational, and diagnostic methods were implemented, such as a review of scientific literature, a compilation of best practices, observation, statistical methods, etc. The authors analyzed the programs of partner universities and organized international webinars and internships for bachelors and masters abroad and developed online courses "Foreign language for undergraduate students and masters". Experimental data obtained during the implementation of cluster training show the effectiveness of the formation of students' polycultural competencies.

• Journal of the Korean Home Economics Association
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• v.44 no.10
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• pp.9-20
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• 2006

Due the globalization trends of industry environment, not only fashion companies of the developed countries but also those of NICs and the developing countries are pursuing to globalize their businesses. This study was conducted (i) to identify the characteristics of international marketing mix of Korean fashion companies operating in oversea fashion markets and (ii) to analyze the performance of Korean fashion companies related to international marketing mix. The data were collected from inter-Korean fashion enterprises. A questionnaire was distributed to a person in charge of international division/international trade division who doing business in domestic market and a person in charge of a local subsidiary in oversea market. The methods of analysis used in this study were factor analysis, cluster analysis, and one-way ANOVA. The results of this study are as follows: First, according to factor analysis and cluser analysis, Korean fashion companies were classified in four (4) clusters. The fashion companies in Cluster I put their priority on price. The companies in Cluster II are traditional fashion companies which have relatively low power of product and price. The companies in Cluster III put their priority on product, and the companies in Cluster IV put their priority on local market. Second, according to ANOVA, a growth rate of sales make significant difference among 4 clusters and Clusters II and III were comparatively high in performance.

• Fashion & Textile Research Journal
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• v.9 no.5
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• pp.472-477
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• 2007

This study was intended to identify the constructing factors and hierarchy of images in the gathered skirts, which is expected to be helpful in shape classification. The gathered skirts were made by different gathering conditions: three kinds of the gathers ratio(1.5T, 2.0T, 2.5T) and different fabrics(cotton, mixed wool, polyester). 45 undergraduate and graduate women students responded to the nine gathered skirts during December in 2004 to February in 2005. 184 words expressing gathered skirt were collected through the investigation and analysis of questionnaires. 32 words arranged in based on the standard form with frequency before conducting factor analysis to identify the constructing factors of gathered skirt images. As a result of factors analysis, 2 factors-H shape, A shape were found out as constructing factors of gathered skirts. To explain the hierarchy of gathered skirt images, cluster analysis was applied. To observe the association of 32 words, dendrogram was introduced, and to interpret the result, five sub clusters were determined. This 5 clusters were continuously combined according to their frequency, based on the factors marks. Two major division of image clusters were 'simple and neat image', and 'fairly good and feminine image'.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.123-134
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• 2016

Social capitals are getting more important for management in enterprises, especially in small and medium sized enterprises(SMEs). The purpose of this study is to examine the effect of social capitals on the performance of intellectual property. For the empirical analysis, survey data were collected from 138 companies in one industry cluster, 34 companies in the other one. The data were compared using regression analysis. The findings confirm a positive influence of the conceptive social capital on the performance of intellectual property while there are no effect of structural and relational social capitals on the performance. These are different from the past researches. In addition, there is no difference between industrial types such as IT, BT, and NT in terms of the effect of social capitals on the performance of intellectual property. Also there is no difference between the two industry clusters.

• Journal of the Korea Convergence Society
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• v.8 no.7
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• pp.39-46
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• 2017

Dongguan City of Guangdong province, one of the core areas of the Pearl River Delta, has also pursued economic development through the geographical advantage close to Hong Kong. In the early 1980s, small and medium-sized multinational corporations related to home appliances industry from Hong Kong invested to the Dongguan area and set up a production factory. In the mid-1990s, as Taiwanese PC manufacturers invested, local industrial clusters have developed in Dongguan with core of the IT, PC components and electronic industries. The case of the IT industrial cluster in Dongguan is a typical example of the development of Chinese manufacturing industry after the reform of China. This paper focused on the coupling strategy case of Dongguan City industrial cluster in Guangdong province, and theoretically compared the endogenous growth factor analysis(NMID) of regional industrial development with the regional differentiation of industry based on external linkage with global production network(GPN).

• Journal of the Korean association of regional geographers
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• v.15 no.5
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• pp.537-553
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• 2009

This study aims to present promotion strategies for regional industries by exploring the ways to build industrial cluster focusing on regional strategic industries of Gyeongsanbuk-do(province) which are related to innovation city, by taking Gyeongbuk innovation city as an example. This study presented the methods for linking with innovation cities that focus on regional strategic industries, along with the analysis on the linkage between regional industries and public organizations relocated to local regions. As to the methods for the linkage, methods to build clusters based on the characteristics of each industry, such as electronic information device, new material parts, biological oriental medicine, cultural tourism, eco-friendly energy, etc, which are strategic and leading industries of Gyeongsanbuk-do(province), were presented. It was inferred that the industries which have achieved fast growth such as IT and BT industries, required mutually interconnected collaboration through geographical proximity among related subjects, while sectors with mature technologies, such as automative parts, machinery, steel industries, etc, were found to require more extensive infrastructures like the support of transportation and distribution for promoting current clusters.

• Fashion & Textile Research Journal
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• v.9 no.6
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• pp.637-644
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• 2007

The purposes of this study were to classify adolescents by their lifestyles and to investigate the differences in clothing purchase behavior among lifestyle groups. The data were obtained from questionnaire completed 341 adolescents in Daegu area. The SPSS package was used for data analysis which included frequency, factor analysis, cluster analysis, ${\chi}^2$ test, ANOVA, and Scheffe test. Lifestyles of adolescents were categorized into five factors such as clothing hedonic shopping orientation, positive activity, material orientation, frugality, digital orientation. Three clusters (achievement orientation group, ordinary group, economic orientation group) were developed by five factors of lifestyles. While the achievement orientation group had the highest purchase motives and used the most information sources, the economic orientation group had the lowest purchasing motives and used the least information sources. There were significant differences in clothing purchase frequency and average clothing expenditure among three clusters.

• Fashion & Textile Research Journal
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• v.6 no.5
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• pp.585-594
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• 2004

This study aimed to classify the lifestyles of 1020 college students, and to analyze the effect of the lifestyles on the usages of credit card, price perception, purchasing satisfaction and the number of new clothing. The number of credit cards, total payment of credit cards and the attitude to credit card were different by the 7 clusters of college students, but the frequency of credit card use, the amount of cash service and arrear ages were not different. The perception to the apparel price, purchasing satisfaction, and the degree of clothing purchasing varied according to the lifestyle clusters. For example, 6th cluster being highest in the pursuit of appearance showed the highest amount of credit cards usage significantly and tendency of highest arrearage, and used credit cards mostly in clothing purchase.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.35-41
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• 2018

In this paper, we propose a swarm-based possibilistic c-means(PCM) algorithm in order to overcome the problems of PCM, which are sensitiveness of clustering performance due to initial cluster center's values and producing coincident or close clusters. To settle the former problem of PCM, we adopt a swam-based global optimization method which can be provided the optimal initial cluster centers. Furthermore, to settle the latter problem of PCM, we design an adaptive thresholding model based on the optimized cluster centers that yields preliminary clustered and un-clustered dataset. The preliminary clustered dataset plays a role of preventing coincident or close clusters and the un-clustered dataset is lastly clustered by PCM. From the experiment, the proposed method obtains a better performance than other PCM algorithms on a simulated magnetic resonance(MR) brain image dataset which is corrupted by various noises and bias-fields.