What is CRM(Customer Relationship Management) means that planning, executing, and re-accessing the marketing strategy based on the customer character by analyzing the material related to customers. That is CRM is a strategy of customer service on the base of data. In the case of the telecommunications and a newspaper, there are restricted application of CRM, because they are provided services by paying a given amount of money within a given period of time. This paper develops CRM model(chum prediction model) that can apply to a newspaper. For model-building, real data were used which were collected from one of the major a newspaper company in Korea. Also, this paper verifies the efficient result.

본 연구는 한라산국립 공원 탐방객들의 탐방 후 평가를 통하여 만족도, 만족요인과 예측모형 을 분석하여 실질적인 공원관리의 기초자료를 제공하는데 목적이 있다. 만족도에서는 위생시설의 청결성, 편익 및 안내시설의 부족 등이 가장 불만족요인으로 나타났고, 만족요인으로는 '경상적 탐방관리요인'이 기여율 46.8%로 만족도에 가장 큰 영향을 미치는 것으로 나타났고, 만족도 예측모형에 있어서는 위생시설부족, 쓰레기문제, 교통시설 안전성 등이 만족도에 영향을 미치는 예측모형을 만들었다.

ETCS는 1990년대 초부터 선진국에서 유료도로 톨게이트에서의 교통체증 및 지체문제를 해결하기 위하여 적용되기 시작하였다. ETCS의 성공적인 운영을 위해서는 ETCS 이용률을 정확히 예측하는 것이 중요하다. 본 연구에서는 창원시의 신뢰성 있는 ETCS 이용수요예측 모형 개발이 시도되었다. 총 11개의 설명변수들을 고려하면서 이항로짓 및 신경망 모형들이 검토되었다. 신경망모형에서 가장 좋은 결과를 얻었다. 그러나 ETCS 이용률 추정을 위해 설명변수 11개를 추정하기에는 어려움이 있으므로 차내장치 임대보증금, 절약시간, 할인률의 중요 3개 정책변수만을 고려한 신경망 및 로짓 모형들을 ETCS 이용률 추정모형으로 분석하였다. 모형의 예측력 평가와 적합도 분석에 있어서 신경망모형이 로짓모형보다 우수한 모형으로 판단되어지나 학습에 사용된 9가지 이외의 시나리오에서 신뢰성이 떨어지는 것으로 나타나, ETCS 이용률 추정 모형으로 3개의 중요한 정책변수를 고려한 이항로짓모형이 제안된다. 이 모형은 평균제곱오차 0.14, σ20.31, x2488.9로 신뢰성이 높은 모형으로 평가되며 창원시와 규모가 유사한 도시에 적용이 가능할 것으로 기대된다.

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.

본 연구는 월출산국립공원 탐방객들의 탐방후 평가를 통하여 만족도, 만족요인과 예측모형을 분석하여 바람직한 공원관리의 기초자료를 제공하는데 그 목적이 있다. 만족도에서는 '탐방프로그램의 부족'이 가장 높았고 시설 부족, 탐방료 부담감과 탐방 팸플릿 및 안내시설 부족 등에 다소 불만족하는 것으로 나타났다. 만족요인으로는 '시설관리요인'과 '경상관리요인'이 만족도에 가장 큰 영향을 미치는 것으로 나타났고, 만족도 예측모형에 있어서는 등산로 훼손 및 안전시설 부족 등 공원의 특성과 부합되는 변수가 만족도에 가장 큰 영향을 미치는 것으로 나타났다.

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 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.

본 연구는 월악산국립공원을 방문한 탐방객들의 탐방만족도를 통하여 만족요인과 예측모형 을 분석하여 효율적인 공원관리의 기초자료를 제공하는데 있다. 만족도에서는 이용료부담감, 시설청결 및 부족, 바가지요금, 쓰레기 문제에 다소 불만족하는 것으로 나타났다. 만족요인으로는 '일반관리요인'이 만족도에 가장 큰 영향을 미치는 것으로 나타났고, 예측모형에 있어서도 휴양활동의 안전, 주차문제, 쓰레기 문제 등이 만족도에 큰 영향을 미치는 변수인 것으로 나타났다.

Customer segmentation prediction has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. However, ANN approaches have suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning technique, support vector machines (SVM), to the customer segmentation prediction problem in an attempt to provide a model with better explanatory power. To evaluate the prediction accuracy of SVM, we compare its performance with logistic regression analysis and ANN. The experiment results with real data of insurance company show that SVM superiors to them.

The purpose of this study was to develop a statistical model of predicting item difficulty of the English reading test of College Scholastic Ability Test(CSAT). At the initial phase, the study investigated variables that were significantly correlated to item difficulty of English reading test. Using the correlated variables an instrument was designed to gather the data on item difficulty of each item of the English reading tests of 2002 and 2003 CSAT. Correlation analysis was done to get models which could predict item difficulty of the tests. To validate the results of the analysis, the models were applied to the September Tests of 2003 and 2004 CSAT. It was found that the 2003 linear regression equation model showed the acceptable level of stability and predictability. The study also revealed that statistically significant predictors were grammar, inference and options. It was also found that the proportion of variance accounted by the predictors was different. Some implications for CSAT design and further research were drawn from the findings.