• Proceedings of the KSRS Conference
• /
• 2007.03a
• /
• pp.301-304
• /
• 2007

본 연구에서는 폐탄광 지역에서 발생하는 지반침하에 영향을 주는 주요 요인들을 추출하기 위하여 다변량 통계분석 방법의 하나인 주성분분석(Principle Component Analysis : PCA)기법과 지리정보시스템 (Geographic Information System : GIS)을 이용하였다. 이를 위해 연구지역에서 수행한 지표지질조사， 정밀조사, 실내암석시험 등으로부터 취득된 자료를 데이터베이스로 구축하고, 지반침하 위험지역 분포를 공간적으로 해석할 수 있는 지질, 토지이용, 경사도， 지표로부터 지하 갱도까지의 심도, 갱도의 지표상 위치로부터의 수평거리, 지하수심도, 투수계수, RMR(Rock Mass Rating) 값을 분석대상으로 선정하였다. 각 요인들이 연구지역 전체에 걸쳐 분포하도록 GIS의 공간분석 기법의 하나인 표면분석(Surface Analysis), 버퍼링기법(Buffering) 및 내삽법(Interpolation)을 이용하여 래스터 데이터베이스로 구축하고 이로부터 추출된 자료들을 입력값으로 하는 주성분분석을 수행하였다. 주성분분석 결과 폐탄광 지역의 지반침하에 영향을 주는 주요인을 추출하는 것이 가능하였으며, 연구지역은 지질 및 지반강도 관련 요인이 침하발생의 가장 큰 요인인 것으로 분석되었다.

• The Korean Journal of Applied Statistics
• /
• v.26 no.6
• /
• pp.933-942
• /
• 2013

Common factor analysis and principal component analysis represent two technically distinctive approaches to exploratory factor analysis. Much of the psychometric literature recommends the use of common factor analysis instead of principal component analysis. Nonetheless, factor analysts use principal component analysis more frequently because they believe that principal component analysis could yield (relatively) less accurate estimates of factor loadings compared to common factor analysis but most often produce similar pattern of factor loadings, leading to essentially the same factor interpretations. A simulation study is conducted to evaluate the relative performance of these two approaches in terms of factor pattern recovery under different experimental conditions of sample size, overdetermination, and communality.The results show that principal component analysis performs better in factor recovery with small sample sizes (below 200). It was further shown that this tendency is more prominent when there are a small number of variables per factor. The present results are of practical use for factor analysts in the field of marketing and the social sciences.

• The Journal of Bigdata
• /
• v.5 no.2
• /
• pp.135-143
• /
• 2020

This paper proposes a pre-processing method and a dimensional reduction method in the analysis of shopping carts where there are many correlations between variables when dividing the types of consumers in the agri-food consumer panel data. Cluster analysis is a widely used method for dividing observational objects into several clusters in multivariate data. However, cluster analysis through dimensional reduction may be more effective when several variables are related. In this paper, the food consumption data surveyed of 1,987 households was clustered using the K-means method, and 17 variables were re-selected to divide it into the clusters. Principal component analysis and factor analysis were compared as the solution for multicollinearity problems and as the way to reduce dimensions for clustering. In this study, both principal component analysis and factor analysis reduced the dataset into two dimensions. Although the principal component analysis divided the dataset into three clusters, it did not seem that the difference among the characteristics of the cluster appeared well. However, the characteristics of the clusters in the consumption pattern were well distinguished under the factor analysis method.

• Proceedings of the KSEEG Conference
• /
• 2007.04a
• /
• pp.549-551
• /
• 2007

• The Korean Journal of Applied Statistics
• /
• v.19 no.3
• /
• pp.587-598
• /
• 2006

The number of variables exceeds the number of samples in microarray data. We propose a nonparametric local linear logistic classification procedure using orthogonal components for classifying high-dimensional microarray data. The proposed method is based on the local likelihood and can be applied to multi-class classification. We applied the local linear logistic classification method using PCA, PLS, and factor analysis components as new features to Leukemia data and colon data, and compare the performance of the proposed method with the conventional statistical classification procedures. The proposed method outperforms the conventional ones for each component, and PLS has shown best performance when it is embedded in the proposed method among the three orthogonal components.

• Journal of Fisheries and Marine Sciences Education
• /
• v.28 no.2
• /
• pp.465-472
• /
• 2016

Principal Component Analysis (PCA) is useful statistical technique for finding patterns in data, and expressing the data in such a way as to highlight their similarities and differences. In this paper, 1417 marine casualties occurred in Korean littoral sea in recent 5 years, were examined by the PCA. The main results obtained were as follows : 1. Most of marine casualties resulted from the human factors such as careless operation and insufficient engine maintenance. 2. Collision and standing mainly resulted from steering room-related human factors such as careless guard, inadequate ship-handling, however engine damage and fire explosion mainly resulted from engine room-related human factor such as bad handling of engine system. 3. No. 1 principal component represents accident frequency, No. 2 principal component represents the cause and No. 3 principal component represents the pattern of marine casualties, respectively.

• Journal of Korea Technology Innovation Society
• /
• v.13 no.2
• /
• pp.231-251
• /
• 2010

The numbers of SCI paper or patent in science and technology are expected to be related with the number of researcher and knowledge stock (R&D stock, paper stock, patent stock). The results of the regression model showed that severe multicollinearity existed and errors were made in the estimation and testing of regression coefficients. To solve the problem of multicollinearity and estimate the effect of the independent variable properly, principal component regression model were applied for three cases with S&T knowledge production. The estimated principal component regression function was transformed into original independent variables to interpret properly its effect. The analysis indicated that the principal component regression model was useful to estimate the effect of the highly correlate production factors and showed that the number of researcher, R&D stock, paper or patent stock had all positive effect on the production of paper or patent.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.3
• /
• pp.317-322
• /
• 2011

Korean adults' internet addiction diagnosis measure, K-scale developed by Korea National Information Society Agency (NIA), has composed of 4 categories including 20 items. This scale can diagnose user's internet addiction with individual's questionnaire items. Most of previous research works were tried to know reasons of internet addiction and to judge whether adolescents are addicted or not with their samples. In this research, it is the goal to find the key component to judge individual's internet addiction by using a decision tree in the data mining field and a principal component analysis in statistics. From the experimental results, we would discover that tolerance and preoccupation factor is the most important one to affect adult's internet addiction.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.3
• /
• pp.293-298
• /
• 2015

The vessel encounter data collected from the vessel trajectories in the maritime traffic situation is possible to analyze vessel collision and near-collision risk using statistical method. In this study, analyzing variables extracted from the vessel encounter data using factor analysis, we determine main factors effecting vessel collision risk from vessel encounter data. In order to calculate each factor, it used principal component analysis for factor analysis after normalization and standardization of vessel encounter variables. As a result of the factor analysis, main effect factors are summarized into the vessel approach factor and collision avoidance variance factor.

• 한국체육학회지인문사회과학편
• /
• v.57 no.4
• /
• pp.387-394
• /
• 2018

The purpose of this study is to provide information regarding the strategies by identifying the main variables that determines the winning team based on the records of all games of the 2017 IIHF World Championship Top league. 64 matches were analyzed for the study. 6 variables were analyzed which included ratio of saves, shots on goal, penalties in minutes, time for power play, power play goals, and face off wins. Logistic regression analysis (LRA), multiple regression analysis (MRA), and principal component analysis (PCA) were implemented to examine the relationship between win and loss. In case of LRA, shots on goal (p<.001), face-off wins (p<.001) had significantly positive relation to winning of game whereas, penalties in minutes (p<.01) and time on power play (p<.01) had significantly negative. Using MRA, win percentage was calculated which had significant positive correlation to ratio of saves (p<.01) and face-off wins (p<.001) whereas, a significant negative with penalties in minutes (p<.001). For PCA, the winning team consisted of penalty, attack, and defense factors whereas, losing teams consisted only the attack and defense factors.