• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6D
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• pp.711-717
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• 2009

This study develops model for analysis of relationship between major node (Interchange in expressway) and land price formation of apartments along with Seoul Belt Expressway by using CHAID analysis. The results show that first, regions(outer side: Gyeongido, inner side: Seoul) on the line of Seoul Belt Expressway are different and a graph generally show llinear relationships between land price and traffic node but it does not; second, CHAID analysis shows two different spatial distribution at the point of 2.6km in the outer side, but three different spatial distribution at the point of 1.4km and 3.8km in the inner side. In other words, traffic access does not necessarily guarantee high housing price since the graphs shows land price related to composite spatial distribution. This implies that residential environments (highway noise and regional discontinuity) and traffic accessibility cause mutual interaction to generate this phenomenon. Therefore, the highway IC landprice model will be beneficial for calculation of land price in New Town which constantly is being built along the highway.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3465-3472
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• 2009

The aims of this study is to medical record data warehouse which had been collected from hospital information systems. continuous patient 2,118 60.5%, informally patient 1,385 39.5%. In using survival factors sex, age, area, insurance, admission-course, medical treatment, out-patient lesson, out-patient form, conference diagnosis, operation, cancer, medical reservation. As a result of making a predictive modeling using the logistic regression, the fitness of the predictive modeling of informally patient was 66.0% and neural network, the predictive was 66.72% and CHAID, the predictive was 63.25%, which is a data mining. The expected modeling of the informally patients, the hospital through the continuous patient management and trust of hospital.

• Proceedings of the Safety Management and Science Conference
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• 2006.11a
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• pp.293-297
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• 2006

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 $\sim$ 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.

• Journal of the Korea Safety Management & Science
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• v.8 no.5
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• pp.17-25
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• 2006

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 $\sim$ 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 Journal of Bigdata
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• v.3 no.1
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• pp.95-103
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• 2018

In recent years, many people in the manufacturing field have been making efforts to increase efficiency while analyzing manufacturing data generated in the process according to the development of ICT technology. In this study, we propose a data mining based manufacturing process using decision tree algorithm (CHAID) as part of a smart factory. We used 432 sensor data from actual manufacturing plant collected for about 5 months to find out the variables that show a significant difference between the stable process period with low defect rate and the unstable process period with high defect rate. We set the range of the stable value of the variable to determine whether the selected final variable actually has an effect on the defect rate improvement. In addition, we measured the effect of the defect rate improvement by adjusting the process set-point so that the sensor did not deviate from the stable value range in the 14 day process. Through this, we expect to be able to provide empirical guidelines to improve the defect rate by utilizing and analyzing the process sensor data generated in the manufacturing industry.

• 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.

• Journal of the Korea Safety Management & Science
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• v.8 no.3
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• pp.1-9
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• 2006

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 ($2002\;{\sim}\;2004$) in korea. According to the result analysis, NN has excellent performance for data analysis and classification of industrial accidents.

• Journal of the Korea Safety Management & Science
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• v.7 no.5
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• pp.1-8
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• 2005

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 ($2002\sim2004$) in Korea.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• The Korean Journal of Emergency Medical Services
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• v.15 no.3
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• pp.57-70
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• 2011

Purpose : The purpose of this study was to reveal some factors of ROSC & survival for cardiac arrests from sport & leisure activities(CASLs). Methods : A retrospective study of the 1,341 out of hospital cardiac arrests(OHCAs) treated by EMS in Gyeonggi Provincial Fire and Disaster Headquarters from January to December in 2008 was conducted. The primary end-point was admission to emergency room. To clarify the factors through comparison of CASLs(n=58) with ROSCs & survivals(n=58), Answer Tree analysis for data mining with the CHAID algorithm was performed and alpha was set at .05. Mean, median, and percentile of time intervals, distances, and age on the 58 CASLs, 75 ROSCs, and 27 survivals(patients admitted to emergency room) were analysed. Results : Fourteen CASLs(24.1%), 41 ROSCs(54.7%), 16 survivals(59.3%) were treated with CPR within 5 min., and only 2 CASLs(3.4%), 11 ROSCs(14.7%), 10 survivals(37.0%) were treated with defilbrillation within 10 min. from arrest. If time recording from arrest to defilbrillation, the patients were classified 81.0%($X^2=9.83$, p=.005) into ROSCs & survivals. And the patients with no history, 100.0%($X^2=5.44$, p=.020). The other patients with no intention, 87.5%($X^2=7.00$, p=.024). Whereas the other patients with intention, treated with CPR after 4 min. from arrest were classified 67.2%($X^2=3.99$, p=.046) into CASLs. Conclusion : CPR within 4 minutes was the most important factor that discriminates between CASLs and ROSCs & survivals to record cardiac arrests-defilbrillation time. CPR within 4 min. from arrest, no history, and no intention were factors for improved ROSC & survival.