• Journal of Korea Port Economic Association
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• v.28 no.4
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• pp.193-207
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• 2012

The of purpose of this study is to predict export and import container volumes using a Decision Tree analysis. Factors which can influence the volume of container cargo are selected as independent variables; producer price index, consumer price index, index of export volume, index of import volume, index of industrial production, and exchange rate(won/dollar). The period of analysis is from january 2002 to December 2011 and monthly data are used. In this study, CRT(Classification and Regression Trees) algorithm is used. The main findings are summarized as followings. First, when index of export volume is larger than 152.35, monthly export volume is predicted with 858,19TEU. However, when index of export volume is between 115.90 and 152.35, monthly export volume is predicted with 716,582TEU. Second, when index of import volume is larger than 134.60, monthly import volume is predicted with 869,227TEU. However, when index of export volume is between 116.20 and 134.60, monthly import volume is predicted with 738,724TEU.

• Informatization Policy
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• v.25 no.2
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• pp.20-45
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• 2018

This study aims to find the factors of Internet overdependence in adults, through the decision tree analysis model, which is a data mining method using National Information Society Agency's raw data from the survey on Internet overdependence in 2016. As a result of the decision tree analysis, a total 16 nodes of Internet overdependence risk groups were identified. The main predicated variables were the amount of time spent per smart media usage in weekdays; amount of time spent per smart media usage in weekends; experiences of purchasing cash items; percentage of using smart media for leisure; negative personality; percentage of using smart media for information search and utilization; and awareness on good functions of the Internet, all of which in order had greater impact on the risk groups. Users in the highest risk node spent the smart media for more than 5 minutes per use and less than 5~10 minutes in weekdays, had experiences of cash item purchase, and had lower level of awareness on the good functions of the Internet. The analysis led to the following recommendations: First, even a short-time use has higher chances of causing Internet overdependence, and therefore, guidelines need to be developed based on research on the usage behavior rather than the usage time. Second, self-regulation is required because factors that affect overindulgence in games, such as the cash items, increase Internet overdependence. Third, using the Internet for leisure causes higher risk of overdependence and therefore, other means of leisure should be recommended.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.729-736
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• 2010

In this paper, we study the influence analysis of admission variables including their characteristics on academic achievements of freshmen at K university in Busan. First, multiple regression analysis is used to examine the main effects of admission variables including students' characteristics on the academic achievements. Also, Decision tree analysis is used to examine the interaction effects for the admission variables on the academic achievements. The results of this paper may be helpful to K university in designing effective admissions strategies for recruiting students.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.217-229
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• 2018

This paper aims at applying the data mining such as decision tree, neural network, and logistic regression to an unsold apartment complex in Wirye new town and developing the model forecasting the result of initial sale contract by house unit. Raw data are divided into training data and test data. The order of predictability in training data is neural network, decision tree, and logistic regression. On the contrary, the results of test data show that logistic regression is the best model. This means that logistic regression has more data adaptability than neural network which is developed as the model optimized for training data. Determinants of initial sale are the location of floor, direction, the location of unit, the proximity of electricity and generator room, subscriber's residential region and the type of subscription. This suggests that using two models together is more effective in exploring determinants of initial sales. This paper contributes to the development of convergence field by expanding the scope of data mining.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.139-155
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• 2003

This paper presents the new methodology of analyzing and classifying patterns of customers in mobile telecommunication market to enhance the performance of predicting the credit information based on the decision tree and neural network. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship and makes special management on the customer who has huh potential of getting out of contract in advance. The real implementation of proposed method shows that the predicted accuracy is higher than existing methods such as decision tree(CART, C4.5), regression, neural network and combined model(CART and NN).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.3
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• pp.518-523
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• 2006

Due to keen competition among companies, they have segmented customers and they are trying to offer specially targeted customer by means of the distinguished method. In accordance, data mining techniques are noted as the effective method that extracts useful information. This paper explores customer segmentation of the home study company using a hybrid decision tree and artificial neural network model. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship. The case study shows that the predicted accuracy of the proposed model is higher than those of regression, decision tree (CART), artificial neural networks.

• Proceedings of the Korean Association for Survey Research Conference
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• 2000.06a
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• pp.93-103
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• 2000

• 한국데이터정보과학회:학술대회논문집
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• 2001.10a
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• pp.1-1
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• 2001

• Proceedings of the Korean Society for Food Science of Animal Resources Conference
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• 2001.06a
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• pp.79-79
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• 2001

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.822-825
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• 2017

본 연구는 키넥트를 통해 사람의 걸음걸이를 측정하고 의사결정트리(Decision Tree)를 통해 분석함으로써 현재의 걸음걸이를 통해 측정자의 허리 또는 무릎에서 발생할 가능성이 높은 문제 또는 질병들을 예측하고 해당결과를 측정자에게 알린다. 본 연구를 진행하며 첫 번째 단계에서는 관련 논문이나 병원 자료 결과들을 통해 판별할 속성들을 정하였다. 두 번째 단계에서는 키넥트를 통해 측정한 실제 데이터를 적용하기에 앞서 첫 번째 단계에서 정한 속성들이 측정자의 문제 또는 질병들을 판단해내는 연관 정도가 높은지 테스트 데이터를 이용하였고 의사결정나무를 통해 분석하였다. 그 결과 7개의 속성 중 6개로 약 85.7%정도의 연관이 있었다. 마지막 세 번째 단계에서는 판별식을 세우고 실제 데이터들을 쌓아나가며 69명의 측정한 데이터를 분석한 결과 6개의 속성 중 5개의 속성이 허리와 연관정도가 높았고 이는 두 번째 단계에서 나왔던 결과인 약85.7%에 가까운 약83%의 결과가 도출되었다. 이를 기반으로 시스템을 개발해 나가며 판별 정확도를 향상시키기 위해 계속 측정해 데이터를 쌓아가고 관련된 식들의 문제점을 보완하며 또한 어떤 환경에서 키넥트의 측정값의 정확도가 올라가는지 연구할 예정이다.