• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.619-631
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• 2018

Functional data analysis continues to attract interest because advances in technology across many fields have increasingly permitted measurements to be made from continuous processes on a discretized scale. Particulate matter is among the most harmful air pollutants affecting public health and the environment, and levels of PM10 (particles less than 10 micrometers in diameter) for regions of California remain among the highest in the United States. The relatively high frequency of particulate matter sampling enables us to regard the data as functional data. In this work, we investigate the dominant modes of variation of PM10 using functional data analysis methodologies. Our analysis provides insight into the underlying data structure of PM10, and it captures the size and temporal variation of this underlying data structure. In addition, our study shows that certain aspects of size and temporal variation of the underlying PM10 structure are associated with changes in large-scale climate indices that quantify variations of sea surface temperature and atmospheric circulation patterns.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.123-140
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• 2023

In recent times, the global economy has been subject to increasing volatility, which has made it considerably more difficult to accurately predict economic indicators compared to previous periods. In response to this challenge, the present study conducts an exploratory investigation that aims to predict the Business Survey Index (BSI) by leveraging data mining techniques on both structured and unstructured data sources. For the structured data, we have collected information regarding foreign, domestic, and industrial conditions, while the unstructured data consists of content extracted from newspaper articles. By employing an extensive set of 44 distinct data mining techniques, our research strives to enhance the BSI prediction accuracy and provide valuable insights. The results of our analysis demonstrate that the highest predictive power was attained when using data exclusively from the t-1 period. Interestingly, this suggests that previous timeframes play a vital role in forecasting the BSI effectively. The findings of this study hold significant implications for economic decision-makers, as they will not only facilitate better-informed decisions but also serve as a robust foundation for predicting a wide range of other economic indicators. By improving the prediction of crucial economic metrics, this study ultimately aims to contribute to the overall efficacy of economic policy-making and decision processes.

• Journal of Digital Convergence
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• v.15 no.10
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• pp.103-111
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• 2017

This study was performed with a view to examine the nature and difference of EFA(Exploratory Factor Analysis) and CFA(Confirmatory Factor Analysis), and to compare the analysis process and result of EFA and CFA with the same data. The result of empirical analysis was as follows. Meanwhile, p.1, p.3 was removed owing to hampering the convergent validity in EFA, p.3 was removed owing to hampering the discriminent validity in CFA. EFA was reduction process of muti measurement variables to a few factor, but CFA was understanding and confirmatory process of measurement and latent variables' relation. Eventually, this study showed that EFA and CFA used different methology, thus the different outcomes appeared although using the same data, and implicated resonable application of methology according to given data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.1
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• pp.36-41
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• 2009

Self organizing map(SOM) is one of the unsupervised methods based on the competitive learning. Many clustering works have been performed using SOM. It has offered the data visualization according to its result. The visualized result has been used for decision process of descriptive data mining as exploratory data analysis. In this paper we propose improvement of SOM using stratified sampling of statistics. The stratification leads to improve the performance of SOM. To verify improvement of our study, we make comparative experiments using the data sets form UCI machine learning repository and simulation data.

• Journal of Information Technology Applications and Management
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• v.27 no.6
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• pp.181-196
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• 2020

Based on the resource-based perspective, this study seeks to understand the relationship between the organizational slack and innovation, and to demonstrate that there exists a difference in the influence of the organizational slack according to the type of innovation by dividing the types of innovation into exploratory and exploitative innovations. They also want to understand the role that network diversity plays in the relationship between organizational slack and innovation. For this purpose, hypothesis and research models were presented based on resource-based perspectives and empirical analysis was conducted on 171 companies. The analysis confirmed that the impact of organizational slack on exploitative innovation is linear, not non-linear, as expected. In other words, the more resources available, the more productive the enterprise is, and the more resources available to the organization have a positive impact on the innovation. On the other hand, exploratory innovation represented an inverse U-shaped relationship between organizational slack and nonlinearity as expected. The control effect of network diversity was only seen in the relationship between organizational slack and exploratory innovation. Through this study, it provides implications such as the importance of network diversity, which is a relationship between companies, and the difference in the utilization of organizational slack according to the type of innovation.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.203-214
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• 2024

This study seeks to provide implications for domestic-related policies through exploratory analysis research to support AI-based policy decision-making. The following should be considered when establishing an AI-based decision-making model in Korea. First, we need to understand the impact that the use of AI will have on policy and the service sector. The positive and negative impacts of AI use need to be better understood, guided by a public value perspective, and take into account the existence of different levels of governance and interests across public policy and service sectors. Second, reliability is essential for implementing innovative AI systems. In most organizations today, comprehensive AI model frameworks to enable and operationalize trust, accountability, and transparency are often insufficient or absent, with limited access to effective guidance, key practices, or government regulations. Third, the AI system is accountable. The OECD AI Principles set out five value-based principles for responsible management of trustworthy AI: inclusive growth, sustainable development and wellbeing, human-centered values and fairness values and fairness, transparency and explainability, robustness, security and safety, and accountability. Based on this, we need to build an AI-based decision-making system in Korea, and efforts should be made to build a system that can support policies by reflecting this. The limiting factor of this study is that it is an exploratory study of existing research data, and we would like to suggest future research plans by collecting opinions from experts in related fields. The expected effect of this study is analytical research on artificial intelligence-based decision-making systems, which will contribute to policy establishment and research in related fields.

• The Journal of Industrial Distribution & Business
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• v.8 no.6
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• pp.7-16
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• 2017

Purpose - Today's world revolves around search engines which are the driving force behind any marketer. The thirst for marketing has led to the evolution of online 'Pay per click' over last few years and is the most widely used instrument. Research design, data, and methodology - Exploratory research design highlights many marketing variables getting affected by pay per click marketing. To analyze the said phenomenon, the data was gathered through questionnaire from the sample of 338 respondents which were selected by simple random sampling method mostly from the National Capital Region (NCR) of Delhi in India. The data collected from the respondents was loaded on SAS base for exploratory factor analysis and multiple regression analysis. Results - Pay per click as a marketing tool has significant impact on the consumers. The most prominent factors of pay per click marketing identified in the research are Ad quality, Competition, Targeting, Trend and Budget. Conclusions - Organic as well as inorganic ads, keeping in mind the end goal to gage the exchange of these two postings in the marked look territory. Additionally, here we dissected supported pursuit promotions in all. It would be beneficial to break down the impact of promotion position on the pay per click marketing.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.951-959
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• 2014

In the past few years, data leakage of information assets has become a prominent issue. According to the National Intelligence Service in South Korea, they found 375 cases of data leakage from 2003 to 2013, especially 49 of cases have been uncovered in 2013 alone. These criminals are increasing as time passes. Thus, it constitutes a reason for establishment and operation of ISMS (Information Security Management System) even for private enterprises. But to be ISMS certified, there are many exposed or unexposed barriers, moreover, sufficient amount of studies has not been conducted on the barriers of ISMS Certification. In this study, we analyse empirically through exploratory factor analysis (EFA) to find the obstacle factors of ISMS Certification. The result shows that there are six obstacle factors in ISMS Certification; Auditing difficulty and period, Consulting firm related, Certification precedence case and consulting qualification, Internal factor, CA reliability and auditing cost, Certification benefit.

• Spatial Information Research
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• v.11 no.3
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• pp.251-260
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• 2003

Distribution pattern of seawater intrusion was analyzed from the spatial distribution map of chloride using the geostatistics and CIS analyses. The chloride distribution map made by kriging(ordinary kriging and co-kriging) after exploratory spatial data analysis. Kriging provides an advanced methodology which facilitates quantification of spatial features and enables spatial interpolation. TDS, Na$^{＋}$, Br$^{[-10]}$ were selected as second parameters of co-kriging which is higher value of correlation coefficients between chloride and others groundwater properties. Chloride concentration is highest in yeminchon and coastal area. And result in co-kriging was accurate than ordinary kriging.

• Journal of Distribution Science
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• v.18 no.12
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• pp.31-43
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• 2020

Purpose: This study examined how many determinant factors (service dimensions, food quality, and price perception) affect revisit intention. This practical concept is service quality (SERVQUAL), customer satisfaction, and repeated/revisit behavioral intention based on the theory of reasoned action (TRA). Research design, data and methodology: This research applied a hybrid mixed-method comprising exploratory and explanatory sequential design by Creswell (2014). The 400 responses were collected in four townships in Myanmar. This study drilled down to exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA) prior to test the hypothesized factor structure of all the variables resulted in the form of the goodness of fit. For further data analysis, structural equation modeling (SEM) was applied to test the relationships among the variables of the proposed model. Results: The results showed that perceived service quality, food quality, and price perception have direct effects on customer satisfaction and indirect effect on revisit intention. The perceived service quality has the most significant influence while the food quality has the least influence on customer satisfaction. Conclusions: The results are useful for the restaurant managers to better understand the significant strategic choice factors to improve higher quality service amongst restaurants both domestic and international under the stiff competition.