Recently, due to economic growth, living environment has been convenient and comfortable and so human activities were decreased. Consequently energy consumption was also decreased. Also the habit of food intake has been changed and the tendency of obesity has been increased. The main objective of this paper is to provide analysis of obesity index of residents in six metropolitan cities. According to analysis of Rohrer Index, there were significant differences regionally between men and women in metropolitan cities. Through analysis of Drop Value, it was found that there were significant differences regionally between men and women in metropolitan cities on circumferences of chest, waist and buttocks. The result of this research can be utilized in manufacturing and design of uniforms and equipments for safety.

Because the damages of corrosion resulting from the chloride ion are very serious, many research studies have been performed to measure the penetration depth of the chloride ion. However, there is a problem with data selection obtained from collection during experiments. After careful study, it appears that the collected data are not conformed to a normal distribution. The result of this study will play a very important role, as a first step for the development and construction of a forecasting system to help determine a reliable service lifetime of marine structures.

The main objective of this study is to suggest the new sizing system proper to the boys in the high school students by classifying their somatotype for the development of educational environment and uniform. The sample for this work was chosen from data which were collected and measured by Size Korea during two years (2003~2004). In order to analyze feature of the somatotype of boys in the high school students, analysis was performed about 479 subjects on 37 body parts such as height (9 parts), width (5 parts), thickness (6 parts), circumference (7 parts), length (8 parts), body weight and Rohrer Index. The result of this study can be utilized in various fields such as design of classroom, student uniforms, facilities and equipments for education at high school and university, etc.

The main objective of this paper is to provide analysis of obesity index of residents in six metropolitan cities. According to analysis of Rohrer Index, there was significant difference regionally between men and women in metropolitan cities. Through analysis of Drop Value, it was found that there were significant differences regionally between men and women in metropolitan cities on circumferences of chest, waist and buttocks. The result of this research can be utilized in manufacturing and design of uniforms and equipments for safety.

The purpose of this study is to provide foot classification on young men and women by factor analysis. The sample for this work was chosen from data which were collected and measured by Size Korea during two years (2003~2004). In order to analyze and compare features of the foot of young men and women, analysis was performed about 911 subjects (male: 467, female: 444) on 24 parts such as height (8 parts), width (5 parts), thickness (1 part), circumference (3 parts), length (3 parts) and angle (4 parts). The result of this study can be applied in manufacturing and design of shoes and socks. Also, it will enable us to have fruitful information on considerable items during manufacturing and design of shoes and socks.

The main objective of this study is to propose numerical guideline for the improvement of educational environment about high school girls. In order to analyze feature of the somatotype of the high school girls, analysis of this study was performed about 25 body parts such as height(7 parts), width(4 parts), thickness(4 parts), circumference(5 parts), length(4 parts), and body weight. For the specific comparison on body dementions, Mollison's comparison graph were used.

Recently most universities are suffering from students leaving their majors. In order to make a countermeasure for reducing major separation rate, many universities are trying to find a proper solution. As a similar endeavor, this paper uses decision tree algorithm which is one of the data mining techniques which conduct grouping or prediction into several sub-groups from interested groups. This technique can analyze a feature of type on students leaving their majors. The dataset consists of 5,115 features through data selection from total data of 13,346 collected from a university in Kangwon-Do during seven years(2000.3.1 2006.6.30). The main objective of this study is to evaluate performance of algorithms including CHAID, CART and C4.5 for classification of students leaving their majors with ROC Chart, Lift Chart and Gains Chart. Also, this study provides values about accuracy, sensitivity, specificity using classification table. According to the analysis result, CART showed the best performance for classification of students leaving their majors.

The main objective of this study is to analyze characteristics on the somatotype of modern adult women In order to standardize of products. The sample for this work chosen from data which were collected and measured by Size Korea during two years(2003~2004). In order to analyze feature of the somatotype of modern adult women, analysis was peformed in 1,371 subjects(645 twenties women, 725 thirties women) about 21 body parts such as height(4 parts), width(3 parts), thickness(3 parts), circumference(7 parts), length(3 parts) and body weight. According to the result of analysis, it was found that the twenties group was near in slender somatotype, whereas the thirties group was near in fat somatotype compared to standard somatotype.

Nowadays most colleges are confronting with a serious problem because many students have left their majors at the colleges. In order to make a countermeasure for reducing major separation rate, many universities are trying to find a proper solution. As a similar endeavor, the objective of this paper Is to find a predicting model of students leaving their majors. The sample for this study was chosen from a university in Kangwon-Do during seven years(2000.3.1 ~ 2006. 6.30). In this study, the ratio of training sample versus testing sample among partition data was controlled as 50% : 50% for a validation test of data division. Also, this study provides values about accuracy, sensitivity, specificity about three kinds of algorithms including CHAID, CART and C4.5. In addition, ROC chart and gains chart were used for classification of students leaving their majors. The analysis results were very informative since those enable us to know the most important factors such as semester taking a course, grade on cultural subjects, scholarship, grade on majors, and total completion of courses which can affect students leaving their majors.

의사결정나무 알고리즘은 데이터마이닝 기법중 하나인데 관심이 되는 데이터들에 대하여 분류 및 예측을 가능하게 해준다. 이 기법은 데이터 형태의 특성을 분석할 수 있고 산업재해 형태의 차이점을 찾아내는데 사용될 수 있다. 본 연구에서는 산업재해 데이터의 특성을 파악하고자 C4.5 알고리즘을 사용하였다. 본 연구에서 분석을 위하여 사용된 데이터는 강원도에서 발생한 2년 동안의 산업재해 관련 데이터로서 연구에 적용된 데이터의 수는 19,909개로 구성되어 있다

Recently data mining techniques have been used for analysis and classification of data related to industrial accidents. The main objective of this study is to compare algorithms for data analysis of industrial accidents and this paper provides an optimal predicting model of 5 kinds of algorithms including CHAID, CART, C4.5, LR (Logistic Regression) and NN (Neural Network) with ROC chart, lift chart and response threshold. Also, this paper provides an approximation model for an optimal predicting model based on NN. The approximation model provided in this study can be utilized for easy interpretation of data analysis using NN. This study uses selected ten independent variables to group injured people according to a dependent variable in a way that reduces variation. In order to find an optimal predicting model among 5 algorithms, a retrospective analysis was performed in 67,278 subjects. The sample for this work chosen from data related to industrial accidents during three years (2002 ~ 2004) in korea. According to the result analysis, NN has excellent performance for data analysis and classification of industrial accidents.

The main objective of this study is to provide feature analysis of industrial accidents in manufacturing industries using CART algorithm, a data mining technique. In this study, data on 10,536 accidents were analyzed to create risk groups, including the risk of disease and accident. Also, this paper used the gains chart produced by the decision tree. According to the result, gains chart can be used for a risk analysis for industrial accidents management. The sample for this work chosen from data related to manufacturing industries during three years (2002～2004) in Korea. The resulting classification rules have been incorporated into development of a developed database tool to help quantify associated risks and act as an early warning system to individual industrial accident in manufacturing industries.

The data mining technique is an effective instrument for making large datasets accessible and different industrial accident data comparable. Many research studies have been focused on the analysis of industrial accidents in order to reduce them. However most researches used a typical technique for the analysis of data related to industrial accidents. The main objective of this study is to compare algorithms comparison for data analysis of industrial accidents and this paper provides a comparative analysis of 5 kinds of algorithms including CHAID, CART, C4.5, LR (Logistic Regression) and NN (Neural Network). This study uses selected nine independent variables to group injured people according to a dependent variable in a way that reduces variation. In this study, data on 10,536 accidents were analyzed to create risk groups for a number of complications, including the risk of disease and accident. The sample for this work chosen from data related to manufacturing industries during three years (2002 ~ 2004) in korea. According to the result analysis, NN has excellent performance for data analysis and classification of industrial accidents.

So far, there is no technique of quantitative evaluation on danger related to industrial accidents. Therefore, as an endeavor for obtaining technique of quantitative evaluation, this study presents feature analysis of industrial accidents in manufacturing field using QUEST algorithm. In order to analyze feature of industrial accidents, a retrospective analysis was performed in 10,536 subjects (10,313 injured people, 223 deaths). The sample for this work chosen from data related to manufacturing businesses during three years (2002~2004) in Korea. The analysis results were very informative since those enable us to know the most important variables such as occurrence type, company size, and occurrence time which can affect injured people. Also, it is found that classification using QUEST algorithm which was performed in this study is very reliable.

Recently data mining techniques have been used for analysis and classification of data related to industrial accidents. The main objective of this study is to compare performance of algorithms for data analysis of industrial accidents and this paper provides a comparative analysis of 5 kinds of algorithms including CHAID, CART, C4.5, LR (Logistic Regression) and NN (Neural Network) with ROC chart, lift chart and response threshold. In this study, data on 67,278 accidents were analyzed to create risk groups for a number of complications, including the risk of disease and accident. The sample for this work chosen from data related to manufacturing industries during three years (2002~2004) in korea. According to the result analysis, NN has excellent performance for data analysis and classification of industrial accidents.

The main objective of this study is to provide feature analysis of industrial accidents in manufacturing industries using CHAID algorithm. In this study, data on 10,536 accidents were analyed to create risk groups, Including the risk of disease and accident. The sample for this work chosen from data related to manufacturing industries during three years (2002~2004) in Korea. The resulting classification rules have been incorporated into development of a developed database tool to help quantify associated risks and act as an early warning system to individual industrial accident in manufacturing industries.

Many researches and analyses have been focused on industrial accidents in order to predict and reduce them. As a similar endeavor, this paper is to develop an expert system for prevention of industrial accidents. Although various previous studies have been performed to prevent industrial accidents, these studies only provide managerial and educational policies using frequency analysis and comparative analysis based on data from past industrial accidents. As an initial step for the purpose of this study, this paper provides a comparative analysis of 4 kinds of algorithms including CHAID, CART, C4.5, and QUEST. Decision tree algorithm is utilized to predict results using objective and quantified data as a typical technique of data mining. Enterprise Miner of SAS and Answer Tree of SPSS will be used to evaluate the validity of the results of the four algorithms. The sample for this work was chosen from 10,536 data related to manufacturing industries during three years(2002~2004) in korea. The initial sample includes a range of different businesses including the construction and manufacturing industries, which are typically vulnerable to industrial accidents.

주어진 시스템에서 정보와 정보흐름에 대한 패턴인식을 하기 위해서는, 정보를 내포하고 있는 문맥이 내용에 따라서 다른 단어나 다른 정보를 추론하여 원래의미를 전달함에 있어 오도할 수 있기 때문에, 문맥의 분해에서 정보 조각의 묶음 형태로 전환하는 작업에서부터 연구는 시작되어야만 한다. 많은 연구자들이 정보의 저장, 재표현, 부호화, 검색 등에 관해 효과적인 방법론을 찾고자 노력해 오고 있다. 유사한 노력의 일환으로 본 논문에서는 군이론과 상황이론을 응용해

The main objective of the statistical analysis about industrial accidents is to find out what is the dangerous factor in its own industrial field so that it is possible to prevent or decrease the number of the possible accidents by educating those who work in the fields for safety tools. However, so far, there is no technique of quantitative evaluation on danger. Almost all previous researches as to industrial accidents have only relied on the frequency analysis such as the analysis of the constituent ratio on accidents. As an application of data mining technique, this paper presents analysis on the efficiency of the CHAID algorithm to classify types of industrial accidents data and thereby identifies potential weak points in accident risk grouping.

The consequences of rapid industrial advancement, diversified types of business and unexpected industrial accidents have caused a lot of damage to many unspecified persons both in a human way and a material way Although various previous studies have been analyzed to prevent industrial accidents, these studies only provide managerial and educational policies using frequency analysis and comparative analysis based on data from past industrial accidents. The main objective of this study is to find an optimal algorithm for data analysis of industrial accidents and this paper provides a comparative analysis of 4 kinds of algorithms including CHAID, CART, C4.5, and QUEST. Decision tree algorithm is utilized to predict results using objective and quantified data as a typical technique of data mining. Enterprise Miner of SAS and AnswerTree of SPSS will be used to evaluate the validity of the results of the four algorithms. The sample for this work chosen from 19,574 data related to construction industries during three years (2002~2004) in Korea.