• Journal of Digital Convergence
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• v.13 no.7
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• pp.169-175
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• 2015

An effective way to understand the dynamic and time series that follows the passage of time, as valuation is to establish a model to analyze the phenomena of the system. Model of the decision process is efficient clustering information of the total mass of the time series data of the relevant population been collected in a particular number of sub-groups than to look at all a time to an understand of the overall data through each community-specific model determination. In this study, a sub-grouping of the group and the first of the two process model of each cluster by determining, in the following in sub-population characterized by a fusion with heuristic Bayesian clustering techniques proposed a process which can reduce calculation time and cost was confirmed by experiments using actual effectiveness valuation.

• Journal of the Economic Geographical Society of Korea
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• v.13 no.4
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• pp.601-622
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• 2010

The purpose of this paper is to analyze the value chain reorganization of cultural contents industry cluster in Daegu city and its impact on the regional economy. To achieve this goal, this research examined the change of value added structure, institutional milieu and networks among regional innovators and their effect on value chain reorganization of cluster. The result shows that the scope of the value chain generating value added have been extended and the networks among regional innovators have been increased. Such value chain reorganization has a positive effect on the regional economy. In the context of public policy, however, the focus of industrial policies needs to generate cultural and institutional specificities in a given region and to strength Triple helix innovation system.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.235-242
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• 2015

Social network technology has been increasing interest in the big data service and development. However, the data stored in the distributed server and not on the central server technology is easy enough to find and extract. In this paper, we propose a big data management techniques to minimize the processing time of information you want from the content server and the management server that provides big data services. The proposed method is to link the in-group data, classified data and groups according to the type, feature, characteristic of big data and the attribute information applied to a hash chain. Further, the data generated to extract the stored data in the distributed server to record time for improving the data index information processing speed of the data classification of the multi-attribute information imparted to the data. As experimental result, The average seek time of the data through the number of cluster groups was increased an average of 14.6% and the data processing time through the number of keywords was reduced an average of 13%.

• Journal of Digital Convergence
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• v.12 no.10
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• pp.243-252
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• 2014

Many researches about the effect on politics, economics and Sociocultural phenomenon using the social media are in progress. Authors utilized NAVER Trend most famous web browsing service in korea, NAVER Blog social media, NAVER Cafe service and Open Data(API) and also used temperature, humidity index data of Korea Meteorological Administration. This study analyzed a change of the public's emotion in korea using Cluster analysis of vocabulary of taste among its of feelings and senses. K-means clustering was followed by decision of the number of groups which was used Chi-square goodness of fit test and ward analysis. Eight groups was made and it represented sensitive vocabulary. By Discriminant analysis, eight groups decided by Cluster analysis has 98.9% accuracy. The change of the public's emotion has capability to predict people's activity so they can share sensibility and a bond of sympathy developed between them.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.387-394
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• 2013

Data mining is the process of discovering hidden, non-trivial patterns in large amounts of data records in order to be used very effectively for analysis and forecasting. Because hundreds of variables give rise to a high level of redundancy and dimensionality with time complexity, they are more likely to have spurious relationships, and even the weakest relationships will be highly significant by any statistical test. Hence cluster analysis is a main task of data mining and is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. In this paper system implementation is of great significance, which defines a new definition based on information-theoretic entropy and analyse the analogue behaviors of objects at hand so as to address the measurement of uncertainties in the classification of categorical data. The sources were taken from a survey aimed to identify of job guidance from students in high school pyeongtaek. we show how variable precision information-entropy based rough set can be used to group student in each section. It is proved that the proposed method has the more exact classification than the conventional in attributes more than 10 and that is more effective in job guidance for students.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.419-429
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• 2013

The Wireless sensor network(WSN) consisting of a large number of sensors aims to gather data in a variety of environments. The sensor nodes operate on battery of limited power. so, To extend network life time is major goals of research in the WSN. In this paper, we state the key point of a energy consumption with minimum&load balancing. The proposed protocol guarantee balance of number of cluster member nodes using the node memory threshold and optimization of distribution of cluster head using the optimized clustering method. The results show that the proposed protocol could support the load balancing and high energy efficiency by distributing the clusters with a reasonable number of member nodes. The simulation results show that our schme ensure longer life time in WSN as compare with existing schemes such as LEACH and CBLM.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.439-446
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• 2019

The purpose of this study is to analyze the relationship between social development and life satisfaction according to horse riding participation degree of participants. In order to accomplish the purpose of this study, participants of the equestrian program were set as the population and the sample was extracted by the method of Stratified cluster random sampling. The input data were statistically processed according to the purpose of the study using the SPSS / AMOS 22.0 Version Statistical Program for Windows, and the results were as follows. The results of the analysis are as follows. First, there were some differences in demographic characteristics such as horse riding experience, social development, life satisfaction, and participation level. Second, there was a partial relationship between participation level and social development of participants. Particularly, the degree of participation in horse riding experience was related to the activity of social development. Third, the participation level of horse riding experience was analyzed as having no direct influence on participants' life satisfaction. Fourth, it was analyzed that the development of sociality due to participation in horse riding experiences did not directly affect life satisfaction. Fifth, participation in experience of horse riding, social development and life satisfaction were not causally related.

• Korean Journal of Heritage: History & Science
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• v.48 no.2
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• pp.82-95
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• 2015

Cultural heritage contains traditional values and we have to conserve its intrinsic value. But in the other hands it is argued that it's no need to preserve heritage for its own sake, and nowadays we appreciate that active use of heritage is enhancing its value and making position secure in its society. It will need not only to protect heritage, but also to ensure its use, and its economic value are harnessed to the benefit of local communities. We are going to enter upon experience economy through information society and to have a creative economy policy discourse. The effects of globalisation on societies are manifested in the attrition of their values, identities of vernacular heritage. Therefore relationship between development and heritage must be examined. In this article I suggest the methodologies of vitalizing cultural heritage based creative industries, especially through making the creative ecosystem and optimising the performance of the cultural heritage based cluster.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.3
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• pp.420-450
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• 2022

Texas has recently emerged as a phenomenal industrial destination, which attracts headquarters, production facilities, and/or regional centers of global corporations including Tesla and Samsung. Known as "Texit", this trend of corporate investment to Texas has concentrated in highly developed industrial clusters (such as Dallas-Fort Worth, Houston, Austin, and San Antonio) since the early 2000s. Against this background, this paper examines sectoral and geographical characteristics of those clusters, and investigates associated state initiatives that have been introduced and implemented. As a result, key policy measures in the state are found to be aligned to free-market liberalism, diversified incentives, and regional specialization. Unlike Silicon Valley where Bohemian liberalism engendered vibrant entrepreneurship and innovative start-up formation, it is the strong state government that leads industrial cluster upgrading in Texas, while successfully harnessing lower income taxes and deregulation, affordable land and infrastructure, and quality higher education and human capital as regional assets for attracting inward investment.

• Journal of the Korean Regional Science Association
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• v.39 no.1
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• pp.3-20
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• 2023

This study aims to categorize the 14 major gentrified commercial areas of Seoul and analyze their characteristics based on their sense of place. To achieve this, we conducted hierarchical cluster analysis using text data collected from Naver Blog. We divided the districts into two dimensions: "experience" and "feature" and analyzed their characteristics using LDA (Latent Dirichlet Allocation) of the text data and statistical data collected from Seoul Open Data Square. As a result, we classified the commercial districts of Seoul into 5 categories: 'theater district,' 'traditional cultural district,' 'female-beauty district,' 'exclusive restaurant and medical district,' and 'trend-leading district.' The findings of this study are expected to provide valuable insights for policy-makers to develop more efficient and suitable commercial policies.