• 한국안전학회지
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• 제28권1호
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• pp.158-163
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• 2013

While job stress evaluations are reported in the recent surveys upon the nuclear power plants(NPPs), any significant advance in the types of questionnaires is not currently found. There are limitations to their usefulness as analytic tools for the management of safety resources in NPPs. Data mining(DM) has emerged as one of the key features for data computing and analysis to conduct a survey analysis. There are still limitations to its capability such as dimensionality associated with many survey questions and quality of information. Even though some survey methods may have significant advantages, often these methods do not provide enough evidence of causal relationships and the statistical inferences among a large number of input factors and responses. In order to address these limitations on the data computing and analysis capabilities, we propose an advanced procedure of survey analysis incorporating the DM method into a statistical analysis. The DM method can reduce dimensionality of risk factors, but DM method may not discuss the robustness of solutions, either by considering data preprocesses for outliers and missing values, or by considering uncontrollable noise factors. We propose three steps to address these limitations. The first step shows data mining with response surface method(RSM), to deal with specific situations by creating a new method called response surface data mining(RSDM). The second step follows the RSDM with detailed statistical relationships between the risk factors and the response of interest, and shows the demonstration the proposed RSDM can effectively find significant physical, psycho-social, and environmental risk factors by reducing the dimensionality with the process providing detailed statistical inferences. The final step suggest a robust stress management system which effectively manage job stress of the workers in NPPs as a part of a safety resource management using the surrogate variable concept.

• Journal of the Korean Data and Information Science Society
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• 제23권5호
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• pp.983-992
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• 2012

사회지표조사는 주민들이 생각하는 사회 상태를 총체적으로 파악할 수 있는 조사로서 다양한 시책 개발에 있어 지역의 여론을 반영할 수 있는 장점이 있다. 사회지표조사는 사회 변화를 알 수 있는 중요한 척도라고 할 수 있으며, 많은 지자체 (서울시, 인천시, 부산시, 울산시, 경상남도 등)에서 많은 예산과 시간을 들여 조사를 실시하고 있다. 그러나 조사에 대한 분석 결과가 기초통계분석 위주로 되어 있어 실제 사회지표조사 자료를 제대로 활용하고 있지 못하고 있는 실정이므로 데이터마이닝 등의 다양한 방법의 적용이 필요하다. 이에 본 논문에서는 사회지표조사의 효율적인 분석을 위하여 새로운 데이터마이닝 방법론을 제시하고자 한다. 본 논문에서는 매개연관성규칙, k-평균 군집분석, 의사결정나무를 순차적으로 적용하는 3단계 복합 데이터마이닝의 적용 방법을 제안하며, 이를 2010년에 조사된 경상남도 사회지표조사 자료에 적용하고자 한다.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제32권4호
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• pp.555-573
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• 2018

본 연구는 텍스트 마이닝 기법을 활용하여 수학수업에서 나타나는 교사의 감성적 언어를 확인하고자 하였다. 이를 위해 우수 수업 동영상을 활용하여 수업에서 발생하는 교사의 수업 언어 데이터를 수집하였다. 추출한 비정형 데이터에 대한 분석 과정은 데이터 수집, 데이터 전처리, 텍스트 마이닝 분석의 세 가지 단계로 진행하였다. 분석 결과 수학 수업에서 오고가는 담화 중에서 교사의 감성적 반응을 나타내는 언어는 거의 나타나지 않았으며, 이를 통해 수업의 정의적 영역 측면에서의 시사점을 도출하였다.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.335-344
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• 2006

The purpose of this paper is to apply data mining method for the inbound call center optimization. Data mining analysis is come to be used in order to predict the degree of difficulty on the consultation. It is the method of maximal efficiency for the call center that uses of the predicted degree of difficulty and customer grade as routing which hits to the skill of the consultation unit. This method is to get the possibility of efficiency for the call center with the maximum efficiency.

• Journal of the Korean Data and Information Science Society
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• 제14권4호
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• pp.737-749
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• 2003

This Paper analyzed simultaneous activities of the time use survey by Korea National Statistical Office to use data mining's association rule. The survey of National Statistical Office in 1999 considered general analysis for main activities like that personal care(eating), employment and study, leisure, travel by purpose. But if we use the association rule, we can found the ratio of simultaneous activities at the same time. And also we can found the probability that another activities practise if we act one particular activity. Using this association rule of data mining we can do more developed and analytical sociological study.

• 경영과학
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• 제19권1호
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• pp.189-196
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• 2002

Data clustering is often one of the first steps in data mining analysis. It Identifies groups of related objects that can be used as a starling point for exploring further relationships. This technique supports the development of population segmentation models, such as demographic-based customer segmentation. This paper Purpose to present the development of two phase hierarchical clustering algorithm for group formation. Applications of the algorithm for product-customer group formation in customer relationahip management are also discussed. As a result of computer simulations, suggested algorithm outperforms single link method and k-means clustering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권12호
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• pp.4467-4488
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• 2014

The bibliometric analysis is widely used for understanding research domains, trends, and knowledge structures in a particular field. The analysis has majorly been used in the field of information science, and it is currently applied to other academic fields. This paper describes the analysis of academic literatures for classifying research domains and for suggesting empty research areas in the telecommunications policy. The application software is developed for retrieving Thomson Reuters' Web of Knowledge (WoK) data via web services. It also used for conducting text mining analysis from contents and citations of publications. We used three text mining techniques: the Keyword Extraction Algorithm (KEA) analysis, the co-occurrence analysis, and the citation analysis. Also, R software is used for visualizing the term frequencies and the co-occurrence network among publications. We found that policies related to social communication services, the distribution of telecommunications infrastructures, and more practical and data-driven analysis researches are conducted in a recent decade. The citation analysis results presented that the publications are generally received citations, but most of them did not receive high citations in the telecommunications policy. However, although recent publications did not receive high citations, the productivity of papers in terms of citations was increased in recent ten years compared to the researches before 2004. Also, the distribution methods of infrastructures, and the inequity and gap appeared as topics in important references. We proposed the necessity of new research domains since the analysis results implies that the decrease of political approaches for technical problems is an issue in past researches. Also, insufficient researches on policies for new technologies exist in the field of telecommunications. This research is significant in regard to the first bibliometric analysis with abstracts and citation data in telecommunications as well as the development of software which has functions of web services and text mining techniques. Further research will be conducted with Big Data techniques and more text mining techniques.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.927-935
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• 2007

Currently, Gyeongnam province is executing the social index survey every year to the provincials. But, this survey has the limit of the analysis as execution of the different survey per 3 year cycles. The solution of this problem is data fusion. Data fusion is the process of combining multiple data in order to provide information of tactical value to the user. But, data fusion doesn#t mean the ultimate result. Therefore, efficient analysis for the data fusion is also important. In this study, we present data fusion method of statistical survey data. Also, we suggest application methodology of association rule mining by clustering through data fusion of statistical survey data.

• 서비스연구
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• 제1권1호
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• pp.61-69
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• 2011

데이터 마이닝은 대용량의 데이터베이스로부터 기존에 알려지지 않은, 즉 단순한 질의어로 추출할 수 없는 형태의 '유용한' 정보를 찾아내고 이를 바탕으로 데이터에 대한 통찰(insight)을 얻는 것으로 정의할 수 있다. 본 논문에서는 웹에서 발생하거나 웹 사이트에 저장한 데이터를 대상으로 유용한 패턴을 찾아내기 위하여 인터넷을 이용하는 이용자의 특징을 분석하기 위해 시도되었다. 즉 인터넷 사용자에 대한 일반적인 통계 정보 데이터에 연관성 분석을 적용하여 인터넷 사용 시간에 영향을 미치는 인터넷 이용자의 특징을 분석하였다. 실험을 통하여 데이터로부터의 연관 규칙을 추출 해내었으며, 최적의 결과를 도출하기위한 데이터 전처리 및 알고리즘을 적용하여 웹 마이닝을 위한 인터넷 사용자의 특징을 분석한 결과 그 유용성을 확인할 수 있었다.

• Smart Structures and Systems
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• 제32권4호
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.