• Korean Journal of Childcare and Education
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• v.18 no.5
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• pp.79-99
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• 2022

Objective: This study was conducted to examine the significant factors affecting media device addiction using the data mining technique for large-scale data from the Panel Study on Korean Children Survey (PSKC). The PSKC data of this study were gathered from the elementary school students in their 10th survey (1,286 3rd grade students). Methods: The SPSS 21.0 program was used for data mining decision tree analysis, and the results are as follows. Results: First, the most important predictor of media device addiction was planning-organization which was among the sub-factors of executive function. Second, as a result of the decision tree analysis, the children with the highest probability of addiction to media devices were ones that had difficulties in planning and organizing, had mothers with a permissive parenting attitude felt difficulties in controlling behavior, and were alone at home for more than two hours a day without any adult supervision. Conclusion/Implications: The results of this study can help guide the direction of future research related to children's addiction to media devices by exploring and analyzing factors that significantly affect children's addiction to media devices.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.28-33
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• 2022

This study aims to investigate factors that affect elderly poverty based on a comprehensive and universal perspective, suggesting some alternatives for improving the poverty rate of the elderly. The comprehensive and universal approach to the poverty of the aged that this study attempts can give a better understanding of the elderly poverty beyond the contribution of the existing literature, with the research model including individual, family, labor, and income factors as the causes of old-age poverty from the comprehensive and universal perspective on the causes of poverty of the elderly. In addition, the study attempts to input variants of variables into the equation for the causes of elderly poverty by using panel data from the 8th Korean Retirement and Income Study. This study employs decision tree analysis to determine the cause of the poverty of the elderly using CHAID. The decision tree analysis shows that the most vital variable affecting elderly poverty is making income. For the poor elderly without earned income, public pensions, educational careers, and residential areas influence elderly poverty, but for the poor elderly with earned income, wage earners and gender are variables that affect poverty. This study suggests some alternatives to improve the poverty rate of the aged. The government should create a better working environment such as senior re-employment for old people to be able to participate in economic activities, improve public pension or social security for workers with unfavorable conditions for public security of old age, and give companies that create employment of the aged diverse incentives.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.463-475
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• 2006

In applications such as a traffic management system, analysis using a spatial hierarchy of a spatial data warehouse and a simple aggregation is required. Over the past few years, several studies have been made on solution using a spatial index. Many studies have focused on using extended R-tree. But, because it just provides either the current aggregation or the total aggregation, decision support of traffic policy required historical analysis can not be provided. This paper proposes hybrid index based on extended aR-tree for the spatio-temporal aggregation. The proposed method supports a spatial hierarchy and the current aggregation by the R-tree. The sorted hash table using the time structure of the extended aR-tree provides a temporal hierarchy and a historical aggregation. Therefore, the proposed method supports an efficient decision support with spatio-temporal analysis and is Possible currently traffic analysis and determination of a traffic policy with historical analysis.

• Journal of Korean Society of Rural Planning
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• v.28 no.4
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• pp.1-9
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• 2022

Rural decline due to the decrease of the local population is an inevitable phenomenon, and a vicious cycle has been formed between a lack of basic living services and a population decrease in rural areas. Therefore, the study aims to derive the migration decision-making characteristics based on basic living service infrastructure data in rural areas. To do this, the population change over the past 20 years was categorized into six types, and the relationship between the classified population change types and the number of basic living service infrastructures was analyzed using decision tree analysis. Of the total 3,501 regions, 801 regions were the continuous decline type, of which 740 were rural areas. On the other hand, among 569 regions that were the continuous increase type, 401 regions were urban areas, confirming the population imbalance between rural and urban areas. As a result of the decision tree analysis on the relationship between population change types and the distribution of basic living service infrastructure, the number of daycare centers was derived as an important variable to classify the continuous increase type. Hospitals, parks, and public transportation were also found to be major basic living services affecting the classification of population change types.

• Journal of Korean Academy of Nursing
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• v.43 no.1
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• pp.1-10
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• 2013

Purpose: The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. Methods: A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. Results: The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. Conclusion: The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.13-20
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• 2007

Decision tree is one of the most useful analysis methods for various data mining functions, including prediction, classification, etc, from massive data. Decision tree grows by splitting nodes, during which the purity increases. It is needed to stop splitting nodes when the purity does not increase effectively or new leaves does not contain meaningful number of records. Pruning is done if a branch does not show certain level of performance. By pruning, the structure of decision tree is changed and it is implied that the previous splitting of the parent node was not effective. It is also implied that the splitting of the ancestor nodes were not effective and the choices of attributes and criteria in splitting them were not successful. It should be noticed that new attributes or criteria might be selected to split such nodes for better tries. In this paper, we suggest a procedure to modify decision tree by Fuzzy theory and splitting as an integrated approach.

• Journal of Industrial Convergence
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• v.15 no.1
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• pp.17-32
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• 2017

The purpose of this study is to evaluate financial models that can predict corporate bankruptcy with diverse studies on evaluation models. The study uses discriminant analysis, logistic model, decision tree, neural networks as analyses tools with 18 input variables as major financial factors. The study found meaningful variables such as current ratio, return on investment, ordinary income to total assets, total debt turn over rate, interest expenses to sales, net working capital to total assets and it also found that prediction performance of suggested method is a bit low compared to that in literature review. It is because the studies in the past uses the data set on the listed companies or companies audited from outside. And this study uses data on the companies whose credibility is not verified enough. Another finding is that models based on decision tree analysis and discriminant analysis showed the highest performance among many bankruptcy forecasting models.

• Journal of the Korea Safety Management & Science
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• v.5 no.3
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• pp.107-119
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• 2003

Expectation and interest about e-CRM are rising for more efficient customer management in on-line including electronic commerce. The decision-making tree can be used usefully as the data mining technology for e-CRM. In this paper, the representative decision making techniques, CART, C4.5, CHAID analyzed the differences in personalization point of view with actuality customer data through an experiment. With these analysis data, it is proposed a new decision-making tree system that has big advantage in personalization techniques. Through new system, it can get following advantage. First, it can form superior model more qualitatively in personalization by adding individual's weight value. Second it can supply information personalized more to customer. Third, it can have high position about customer's loyalty than other site of similar types of business. Fourth, it can reduce expense that cost marketing and decision-making. Fifth, it becomes possible that know that customer through smooth communication with customer who use personalized service wants and make from goods or service's quality to more worth thing.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.243-246
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• 2003

Data mining is a process of discovering useful patterns for decision making from an amount of data. It has recently received much attention in a wide range of business and engineering fields Classifying a group into subgroups is one of the most important subjects in data mining Tree-based methods, known as decision trees, provide an efficient way to finding classification models. The primary concern in tree learning is to minimize a node impurity, which is evaluated using a target variable in the data set. However, there are situations where multiple target variables should be taken into account, for example, such as manufacturing process monitoring, marketing science, and clinical and health analysis. The purpose of this article is to present several methods for measuring the node impurity, which are applicable to data sets with multiple target variables. For illustrations, numerical examples are given with discussion.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.514-516
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• 2016

사람은 개인마다 선호색이 다르다. 때문에 색채를 통해서 개인의 성향을 분석하기도 한다. 일반적으로 난색은 밝고 따뜻한 색으로 활기와 적극성을 띄며 한색은 차갑고 냉정함, 차분함 등과 같은 의미를 지닌다. 이러한 색채가 가지는 의미는 개인의 환경, 성향, 성별, 연령 등에 따라 다르게 나타난다. 색채 선호는 일반적으로 개인이 색채에 대해 좋아하는 정도를 의미하는 것으로 개인의 성향이나 상황, 경험 등에 의해 형성된 지극히 개인적인 색을 말한다. 본 논문에서는 색채 선호를 분석하는 심리 검사 CRR와 Flood Fill 알고리즘을 적용하여 그림에 색채를 채워서 주조색과, 보조색을 각각 Decision Tree에 적용한다. Decision Tree의 결과를 기반으로 데이터베이스와 연동하여 개인의 심리 상태를 분석할 수 있는 방법을 제안한다.