• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2001.01a
• /
• pp.456-463
• /
• 2001

Understanding and adapting to changes of customer behavior in internet shopping mall is an important aspect to survive in continuously changing environment. This paper develops a methodology based on decision tree algorithms to detect changes of customer behavior automatically from customer profiles and sales data at different time snapshots. We first define three types of changes as emerging pattern, unexpected change and the added/perished rule. Then, it is developed similarity and difference measures for rule matching to detect all types of change. Finally, the degree of change is developed to evaluate the amount of change. A Korean internet shopping mall case is evaluated to represent the performance of our methodology. And practical business implications for this methodology are also provided.

• 한국데이터정보과학회:학술대회논문집
• /
• 2006.04a
• /
• pp.241-251
• /
• 2006

Data mining is the method to find useful information for large amounts of data in database It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze industrial waste database using data mining technique. We use k-means algorithm for clustering and C5.0 algorithm for decision tree and Apriori algorithm for association rule. We can use these analysis outputs for environmental preservation and environmental improvement.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.12 no.4
• /
• pp.187-194
• /
• 2008

Recently, the constructions of widening bridges or new bridges are often undergoing as a part of road widening because traffic volumes are rapidly increasing caused by fast-growing population and urbanization. But in general, there is no rational decision process and specification to justify the validity of the bridge widening. Moreover, there are also numerous events including various uncertainties involved in widening bridges. In this paper, therefore, a decision making model is proposed for widening bridges using decision tree based on quantitative LCC analysis considering a variety of uncertainties for the rational and practical approach to a quantitative decision making for alternatives.

• Computers and Concrete
• /
• v.33 no.6
• /
• pp.739-754
• /
• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.171-172
• /
• 2011

In this research, we discuss the process of analyzing the solution of four-peg tower of Hanoi using protocol analysis technique, and of developing decision trees from the analyzed contents. For these, we design and implement a computer program that can simulate four-peg tower of Hanoi. The implemented program generates a certain regular-to-regular tower of Hanoi problem, let a user to solve the problem, and records the solution process. From the process by the implemented program and the recorded protocol, we can build the decision tree for the solution. We expect this research can contribute to find a non-optimal solution for n-peg tower of Hanoi.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.27 no.1
• /
• pp.80-93
• /
• 2024

This study attempted to analyze the factors that influence the participation of beneficiary companies in the government's defense industry promotion support project. To this end, experimental data were analyzed by constructing a prediction model consisting of highly important variables in beneficiary company decisions among various company information using the decision tree model, one of the data mining techniques. In addition, various rules were derived to determine the beneficiary companies of the government's support project using the analysis results expressed as decision trees. Three policy measures were presented based on the important rules that repeatedly appear in different predictive models to increase the effect of the government's industrial development. Using the analysis methods presented in this study and the determinants of the beneficiary companies of the government support project will help create a sustainable future defense industry growth environment.

• International journal of advanced smart convergence
• /
• v.6 no.3
• /
• pp.22-28
• /
• 2017

With the recent development of technologies to manage vast amounts of data, data mining technology has had a major impact on all industries.. Data mining is the process of discovering useful correlations hidden in data, extracting executable information for the future, and using it for decision making. In other words, it is a core process of Knowledge Discovery in data base(KDD) that transforms input data and derives useful information. It extracts information that we did not know until now from a large data base. In the decision tree, c4.5 algorithm was used. In addition, the C4.5 algorithm was used in the decision tree to analyze the difference between frequency and mortality in the region. In this paper, the frequency and mortality of percutaneous coronary intervention for patients with heart disease were divided into regions.

• Journal of Korea Water Resources Association
• /
• v.35 no.5
• /
• pp.597-609
• /
• 2002

This study suggests a methodology of hydrologic decision making for the establishment of a standard of drought management from the drought analysis by the past drought history and for the drought monitoring and management according to drought processing. The construction and analysis of a decision tree diagram are performed and the step by step plan according to drought severity is suggested. Say, the decision tree diagram is constructed by the transition probability and quantity of monthly precipitation. Then the drought processing is investigated by the analysis of diagram and the 3-step of drought notice, drought warning, and emergency plan are established. The suggested methology in this study can be used for the other area and the decision tree diagram be used by changing the diagram according to the utilization purposes. Also, the choice of monthly PDSI class and precipitation analysis can be performed by the continuous data supplement. And so, a new standard value by the modified diagram is provided and the continuous drought management will be possible.

• Journal of Korean Society for Quality Management
• /
• v.48 no.2
• /
• pp.297-307
• /
• 2020

Purpose: The purpose of this study is to analyze the factors that affect ammunition performance by applying data mining techniques to the Ammunition Stockpile Reliability Program (ASRP) data of the 155mm propelling charge. Methods: The ASRP data from 1999 to 2017 have been utilized. Logistic regression and decision tree analysis were used to investigate the factors that affect performance of ammunition. The performance evaluation of each model was conducted through comparison with an artificial neural networks(ANN) model. Results: The results of this study are as follows; logistic regression and the decision tree analysis showed that major defect rate of visual inspection is the most significant factor. Also, muzzle velocity by base charge and muzzle velocity by increment charge are also among the significant factors affecting the performance of 155mm propelling charge. To validate the logistic regression and decision tree models, their classification accuracies have been compared with the results of an ANN model. The results indicate that the logistic regression and decision tree models show sufficient performance which conforms the validity of the models. Conclusion: The main contribution of this paper is that, to our best knowledge, it is the first attempt at identifying the significant factors of ASPR data by using data mining techniques. The approaches suggested in the paper could also be extended to other types ammunition data.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.1
• /
• pp.69-74
• /
• 2021

Internet access is also increasing as online activities increase due to the influence of Corona 19. However, network attacks are also diversifying by malicious users, and DDoS among the attacks are increasing year by year. These attacks are detected by intrusion detection systems and can be prevented at an early stage. Various data sets are used to verify intrusion detection algorithms, but in this paper, CICIDS2017, the latest traffic, is used. DDoS attack traffic was analyzed using the decision tree. In this paper, we analyzed the traffic by using the decision tree. Through the analysis, a decisive feature was found, and the accuracy of the decisive feature was confirmed by proceeding the decision tree to prove the accuracy of detection. And the contents of false positive and false negative traffic were analyzed. As a result, learning the feature and the two features showed that the accuracy was 98% and 99.8% respectively.