• Journal of the Korean Data and Information Science Society
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• 제27권5호
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• pp.1413-1421
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• 2016

In this paper graphical exploratory data analyses are proposed for ball games in sports. The plot of sequence of scoring points of each team can be used to see how the playing game has been processed until the end of each set or quarter. With the plot of sequential score differences through all the games we can see a dominance of each team and the times of score changes, i.e., turnovers. The ternary plots show the contours of scoring compositions for each player and enable us to compare the scoring patterns of each team if any. Using the score sequence plot we also can see the score pattern distribution of players. For demonstration we use the results of the gold medal match between Russia and Brazil for men's volleyball and between USA and Spain for men's basketball at the London 2012 Summer Olympics.

• 한국신뢰성학회지:신뢰성응용연구
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• 제18권1호
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• pp.20-32
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• 2018

Purpose: The purpose of this study is to set up an anomaly detection criteria for sensor data coming from a motorcycle. Methods: Five sensor values for accelerator pedal, engine rpm, transmission rpm, gear and speed are obtained every 0.02 second from a motorcycle. Exploratory data analysis is used to find any pattern in the data. Traditional process control methods such as X control chart and time series models are fitted to find any anomaly behavior in the data. Finally unsupervised learning algorithm such as k-means clustering is used to find any anomaly spot in the sensor data. Results: According to exploratory data analysis, the distribution of accelerator pedal sensor values is very much skewed to the left. The motorcycle seemed to have been driven in a city at speed less than 45 kilometers per hour. Traditional process control charts such as X control chart fail due to severe autocorrelation in each sensor data. However, ARIMA model found three abnormal points where they are beyond 2 sigma limits in the control chart. We applied a copula based Markov chain to perform statistical process control for correlated observations. Copula based Markov model found anomaly behavior in the similar places as ARIMA model. In an unsupervised learning algorithm, large sensor values get subdivided into two, three, and four disjoint regions. So extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior in the sensor values. Conclusion: Exploratory data analysis is useful to find any pattern in the sensor data. Process control chart using ARIMA and Joe's copula based Markov model also give warnings near similar places in the data. Unsupervised learning algorithm shows us that the extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2010년도 춘계학술대회
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• pp.253-258
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• 2010

The paper reviews the methodologies of confirmatory data analysis(CDA) and exploratory data analysis(EDA) in statistical quality control(SQC), design of experiment(DOE) and reliability engineering(RE). The study discusses the properties of flexibility, openness, resistance and reexpression for EDA.

• 응용통계연구
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• 제29권2호
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• pp.355-368
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• 2016

특이점 및 영향점은 자료분석을 하는 데 사용되는 계량적이고 기술적인 많은 측도들을 왜곡한다. 각종 자료분석에 있어서의 특이점 검색을 위한 검정 통계량이나 그림도구에 관한 연구는 꾸준히 전개되어 왔다. Jang과 Anderson-Cook (2014)은 불꽃그림이란 이름을 붙인 그림도구를 발표하였는데 이상점이나 영향점이 일변량/이변량 자료분석 및 회귀분석에 어떠한 영향을 미치는지 알기 위하여 3-D 불꽃그림 및 불꽃그림 행렬을 제시하였다. 본 연구에서는 이러한 불꽃그림이 일변량 자료의 왜도와 첨도에서 특이점의 영향을 평가하기 위한 탐색적 자료분석 그림도구로서 사용될 수 있음을 보였다.

• 한국응용과학기술학회지
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• 제39권6호
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• pp.853-863
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• 2022

본 연구에서는 미용분야 창업 활성화를 위해 소셜 빅데이터 분석을 탐색적 데이터 분석(EDA)을 기반으로 하여 2019년부터 2021년 동안 각 년도별로 기간을 구분하여 '미용창업'에 대한 수요 변화와 감정 및 의미 차이의 특징적인 패턴을 도출하고자 하였다. '미용창업' 키워드를 주제로 연관된 검색어를 추출한 결과 창업에 필요한 전문적인 창업교육 보다는 미용관련 기술을 배울 수 있는 기관이나 자격증에 더 많은 관심을 보였으며, 이는 정부 및 지자체에서 여러 가지 창업지원 정책들이 마련되고 있음에도 불구하고 여전히 전문적인 창업교육의 중요성을 인식하지 못하고 있는 것으로 파악할 수 있으며, 이에 대한 대안으로 미용분야 창업을 성공적으로 이루기 위한 전공별 맞춤형 창업교육 프로그램을 개발하는 것이 필요할 것으로 사료된다. 탐색적 데이터 분석을 통해 가설을 설정하고 전통적인 확증적 데이터 분석(CDA)을 결합하여 가설을 검증한다. 미용 창업을 위한 탐색적 데이터 분석 방법이 존재한 적은 없으며, 정식 창업교육의 필요성을 언급하기보다는 미용창업에 대한 관심 변화와 예비창업자의 요구사항을 탐색적 데이터로 분석한다면 맞춤형 창업 프로그램 개발에 도움이 될 것이라고 확신한다.

• 한국의류산업학회지
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• 제5권3호
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• pp.260-266
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• 2003

The objective of this study is to compare the general body measurements and shoulder shapes of Korean and American elderly women to supply basic data for the apparel design. The anthropometrics data was collected including both direct and indirect measurements of 283 women over the age of 55 in Korean and the American women. The statistical methods used for the analysis of measurement data are the T-test, Exploratory data analysis, ANOVA and Duncan-test respectively. The results of the T-test indicated that there is a significant difference in the 14 body measurement items except of waist circumference. The results of exploratory data analysis, an independent relationship between shoulder slope angle and forward shoulder roll of Korean women. On the other hand, there is a dependent relationship that the bigger shoulder slope and forward shoulder roll with wide cross back shoulder of American women. Comparison of mean among the three different age groups, aged 55~59 group shows significant differences in the value of difference between cross back shoulders and horizontal shoulder width. This finding indicates that the wide and forward roll shoulder needs to special pattern making like ease amount and curvature for fit and comfort for women's apparel.

• 한국과학교육학회지
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• 제17권4호
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• pp.481-492
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• 1997

The purpose of this study is to construct validity of Tripartite model of "Attitudes towards Science" using Exploratory and Confirmatory Factor Analyses. Exploratory and confirmatory factor analyses are two major approaches to factor analysis. The primary goal of factor analysis is to explain the covariances or correlations between many observed variables by means of relatively few underlying latent variables. In exploratory factor analysis, the number of latent variables is not determined before the analysis, all latent variables typically influence all observed variables, the measurement errors(${\delta}$) are not allowed to correlate, and unidentification of parameters is common. Confirmatory factor analysis requires a detailed and identified initial model. Confirmatory factor analysis techniques allow relations between latent and observed variables that are not possible with traditional, exploratory factor analysis techniques. As a result of exploratory factor analysis, tripartite model of "Attitudes towards Science" being composed of affection, behavioral intention and cognition is empirically identified. But attitude of science career being composed of affection and behavioral intention is identified. In validity test using confirmatory factor analysis, measurement structure of Tripartite model of "Attitudes towards Science" is not correspondent to data set. Because it is concluded that the object of attitudes are not specific.

• 복식
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• 제60권1호
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• pp.43-55
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• 2010

To pursue youth and agelessness can be regarded as a global trend today. The younger a woman recognizes herself to be, the more sensation seeking tendency and the more active fashion exploratory behavior of younger generation she would show. This study attempted to empirically examine the relationship between sensation seeking behavior and fashion exploratory behavior according to the difference age in women in their 30's to 50s'. After the survey, a total of 480 questionnaires was used for data analysis. The results of this study are as follows, 1. It was found that there was a very high correlation among cognitive ages, and the lower cognitive age a woman had, the higher difference age she showed. 2. Sensation seeking tendency of adult women was shown in two factors of change seeking and artistic sensation seeking, and these factors accounted for 73.99% of the total variances. Fashion exploratory behavior had 4 factors such as fashion leadership, behavior of hedonic shopping, behavior of clothing communication and behavior of clothing purchase with taking a risk, and these four factors accounted for 75.87% of the total variances. 3. The higher difference age and the higher tendency of sensation seeking an adult woman had, the higher fashion exploratory behavior was shown, and the higher the difference age, the higher tendency of change seeking and artistic sensation seeking.

• Journal of information and communication convergence engineering
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• 제21권3호
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• pp.216-224
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• 2023

Smart lighting systems have become increasingly popular in several public sectors because of trends toward urbanization and intelligent technologies. In this study, we designed and implemented a web application platform to explore and monitor data acquired from lighting devices at Thammasat University (Rangsit Campus, Thailand). The platform provides a convenient interface for administrative and operative staff to monitor, control, and collect data from sensors installed on campus in real time for creating geographically specific big data. Platform development focuses on both back- and front-end applications to allow a seamless process for recording and displaying data from interconnected devices. Responsible persons can interact with devices and acquire data effortlessly, minimizing workforce and human error. The collected data were analyzed using an exploratory data analysis process. Missing data behavior caused by system outages was also investigated.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.303-306
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• 1995

In this study we present the inverse correlation method to select the exploratory variables, while Sugeno used RC method in his paper[6] We assume linear model with measurement error variables as in Fuller's Book[9]. we provide possibilistic linear model and predict the fuzzy response variable in case of fuzzy exploratory variables. By plotting data we can divide them for piecewise plane and provide the piecwise possibilistic linear model. If the exploratory variable is fuzzy trapezoidal variable or interval variable, then we estimate fuzzy trapezoidal variable or interval variable, then we estimate fuzzy trapezoidal response variable respondent to it. We will illustrate using Nonlinear System data in Sugeno's paper