A company establishes a sale strategy through the inventory to set the purchasing requisite of the customer in a global company environment. And a sale company can become the reason of a sale opportunity loss because of a customer satisfaction rate if i

심장의 활동을 기록한 심전도는 심장의 상태에 대한 가치 있는 임상 정보를 제공한다. 지금까지 심전도를 이용한 심장 질환 진단 알고리즘에 대한 많은 연구가 진행되어 왔으나, 심장 질환에 대한 국내 진단 결과의 부정확성 때문에 외국의 진단 알고리즘을 사용하고 있다. 이 논문에서는 원시 심전도 데이터로부터 심장 질환 진단의 파라미터인 ST-segment 추출 방법을 제안한다. ST-segment는 관상동맥 질환 예측에 활용되므로 데이터마이닝의 분류기법을 적용하여 질환을 예측한다. 또한 연관규칙 마이닝을 통해 환자들의 임상 데이터로부터 심장 질환자들의 임상적 특징을 예측한다.

The authors used association rules and patterns in sequential of data mining in order to raise the efficiency of engineering changes. The association rule can reduce the number of engineering changes since it can estimate the parts to be changed. The patterns in sequential can perform engineering changes effectively by estimating the parts to be changed from sequence estimation. According to this result, unnecessary engineering changes are eliminated and the number of engineering changes decrease. This method can be used for improving design quality and productivity in company managing engineering changes and related information.

  According to the automated manufacturing processes followed by the development of computer manufacturing technologies, products or quality characteristics produced on the processes have measured and recorded automatically. Much amount of data daily prod

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.

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.

This paper presents a data-mining aided heuristic algorithm development. The developed algorithm includes three steps. The steps are a uniform selection, development of feature functions and clustering, and a decision tree making. The developed algorithm

급속도로 발전하는 산업의 고도화와 이에 따른 업종의 다양화, 이에 동반되는 예상치 못한 산업재해는 불특정 다수에게 인적, 물적 피해를 야기 시키고 있다. 산업재해 예방을 위해 다양한 선행 연구들이 진행되었으나 이들 연구는 기존의 산업재해 데이터를 토대로 빈도분석, 비교분석을 통한 관리적, 교육적 등치 대책만을 제시하고 있다. 본 연구에서는 산업재해 예방을 위해 객관적이고 정량화된 데이터를 통한 예측 분석이 가능한 데이터마이닝을 적용하여 대표적인 기법인 의사결정나무의 CHAID, CART, C4.5, QUEST 4가지 알고리즘 비교분석하여 산업재해 예방 및 전문가 시스템 구축을 위해 적용할 수 있는 최적의 알고리즘을 제시하도록 한다.

This paper uses a data mining methodologies to improve and predict cause of defect process variables in manufacturing process. Traditional statistical process control (SPC) techniques of control charting are not applicable in many process industries because it is difficult to analyze the cause of many process variables. The paper suggests that data mining methodologies useful when sequence rule, SVM (classification) methods are find out cause of defect process variables and SVM (prediction) methods used to predict process variables in manufacturing process. Therefore, it is allowing improved control in manufacturing process.

Many researches have been focused on the analysis of industry disasters in order to reduce them. As a similar endeavor, this paper provides a propensity analysis of injured people from various industries using classification and regression tree(CART), a data mining algorithm. The sample for this work was chosen from 25,157data related to various industries during one year ( 2003.2~2004.1 ) at Kangwon-Do in Korea. For the purpose of this paper, eight independent variables (injured date, injured time, injured month, type of Injured person, continuous service period, sex, company size, age)are taken from injured person group. According to the analysis result, it is found that five out of the eight factors that are predicted as significant have salient effects. Factors of season, time/hour, day of the week, or month which disasters happened do not show any significant effect. This paper provides common features of injured people. The provided analysis result will be helpful as a starting point for root cause analysis and reduction of industry disasters and also for development of a guideline of safety management.

With development of the database, there are too many data on process variables and the manufacturing process for the traditional statistical process control methods to identify the process variables related with assignable causes. Data mining is useful in

This paper uses a data mining methodologies to improve and predict cause of defect process variables in manufacturing process. Traditional statistical process control (SPC) techniques of control charting are not applicable in many process industries because it is difficult to analyze the cause of many process variables. The paper suggests that data mining methodologies useful when sequence rule, SVM (classification) methods are find out cause of defect process variables and SVM (prediction) methods used to predict process variables in manufacturing process. Therefore, it is allowing improved control in manufacturing process.

This paper presents a data-mining aided heuristic algorithm development. The developed algorithm includes three steps. The steps are a uniform coverage selection, development of feature functions and clustering, and a decision tree making. The developed algorithm is employed in designing an optimal multi-station fixture layout. The objective is to minimize the sensitivity function subject to geometric constraints. Its benefit is presented by a comparison with currently available optimization methods.

효과적으로 공정을 관리하기위하여 제품의 품질특성치에 영향을 주는 데이터를 수집하고 공정을 해석하여한다. 이를 위해서 데이터 마이닝(Data Mining)이 많이 수행되어지고 있다. 본 연구에서는 공정으로부터 수집된 대량의 정보 데이터를 신경망(Neural Network)기법을 통하여 공정의 불량률을 예측하고 불량률이 높게 나타난 데이터를 통해 연관규칙(Association Rule)을 적용하여 불량에 영향을 주는 공정의 패턴을 파악 공정을 개선하고자 한다.

인터넷이 현대사회의 많은 부분을 대체해 감에 따라서 Off-line에서 이루어지던 중고자동차의 매매 역시 On-line에서의 매매가 활발해 지고 있다. 이러한 On-Line에서의 장점은 매매차량등록의 편의성과 지역적 제약을 받지 않는다는 점 등을 들 수 있으며, 이로 인하여 빠른 거래가 성사될 수 있다. 또한 매매상사의 중간마진을 없앰으로써 거래당사자간의 상호이익도 극대화될 수 있다. 반면에 객관적인 검사를 통한 신뢰성의 결여, 이로 인한 적정가격 산출의 어려움, 구매희망자와의 잦은 상담 가능성 등의 단점을 들 수 있다. 본 연구에서는 On-line 거래의 이러한 문제점을 해결하기 위하여 데이터마이닝 기법을 다룬다. 이 데이터마이닝 기법은 의사결정나무와 다중회귀분석을 포함하며, 각각 E-miner와 Statgraphics를 이용하여 분석되었다. E-miner를 활용한 의사결정나무를 도출하기 위하여 DATA의 전처리과정에 따른 비교분석이 수행되었으며, DATA는 학습용과 평가용으로 구분하여 이용되었다. 학습용 DATA에 기반하여 두 가지 기법에 대한 지식과 모델이 각각 도출되었으며, 이들 각각에 대한 비교평가가 이루어졌다. 또한 평가용 DATA에 대하여도 각각의 비교평가가 이루어졌으며. 이에 기초하여 보다 나은 On-line 매매지원시스템이 결정되었다.

Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manuf

In general, data mining has iterative processes with the following five steps: Data Selection, Cleansing, Transformation, Mining, Interpretation. Among these steps, steps of data selection and cleansing are performed to classify data. There are two types of data, continuous data and discrete data. Discrete data has a classified structure and it is easy to obtain rules from data. However, there are no general rules for classified method of data in continuous data. So, the result of data analysis will be differed from the classified method of data in continuous data. This research presents a methodology that can obtain the rules from data and classify data according to situations in DBMS (Data Base Management Systems).

Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manufacturing process. The purpose of this paper is to model the recognition of defect type patterns and prediction of each defect type before it occurs in manufacturing process. The proposed model consists of data handling, defect type analysis, and defect type prediction stages. The performance measurement shows that it is higher in prediction accuracy than logistic regression model.