• Journal of the military operations research society of Korea
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• v.24 no.1
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• pp.18-40
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• 1998

This paper is aimed to design an intelligent military decision aiding system in a network computing environment, especially focusing on designing an intelligent analytic system that has data mining tools and inference engine. Through this study, we concluded that the intelligent analytic system can aid military decision making processes. Highlights of the proposed system are as follows : 1) Decision making time can be reduced by the On-line and Real-time analysis ; 2) Intelligent analysis on military decision problems in network computing environments in enabled; 3) The WWW-based implementation models, which provide a standard user interface with seamless information sharing and integration capability and knowledge repository.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.664-677
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• 2014

Wind energy has proven its viability by the emergence of countless wind turbines around the world which greatly contribute to the increased electrical generating capacity of wind farm operators. These infrastructures are usually deployed in not easily accessible areas; therefore, maintenance routines should be based on a well-guided decision so as to minimize cost. To aid operators prior to the maintenance process, a condition monitoring system should be able to accurately reflect the actual state of the wind turbine and its major components in order to execute specific preventive measures using as little resources as possible. In this paper, we propose a fault detection approach which combines cluster analysis and frequent pattern mining to accurately reflect the deteriorating condition of a wind turbine and to indicate the components that need attention. Using SCADA data, we extracted operational status patterns and developed a rule repository for monitoring wind turbine systems. Results show that the proposed scheme is able to detect the deteriorating condition of a wind turbine as well as to explicitly identify faulty components.

• Journal of Digital Contents Society
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• v.9 no.2
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• pp.251-260
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• 2008

In internet being a repository of massive information, we easily may not find our desired information, this issue also exists in e-commerce which gets rapid growth. In most of e-commerce sites, the methods furnishing information have been made use of statistical analysis or simple process by category-oriented, but these can't represent diverse correlation among products information and also hardly reflect users' purchasing patterns precisely. In this thesis, we propose more efficient web mining ways for discovering interesting information using effective clustering in e-commerce, which get achieved more suitable relationship among products information using both sequential patterns and association rules in category-independent, and experiments show the efficiency of our proposed methods. And we propose search using effective clustering rapidly.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.1-14
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• 2007

Case-based reasoning (CBR) is one of the most widely applied data mining techniques and has proven its effectiveness in various domains. Since CBR is basically based on k-Nearest Neighbors (NN) method, the value of k affects the performance of CBR model directly. Once the value of k is set, it is fixed for the lifetime of the CBR model. However, if the value is set greater or smaller than the optimal value, the performance of CBR model will be deteriorated. In this research, we propose a new method of composing the NN set using similarity scores as themselves, which we shall call s-NN method, rather than using the fixed value of k. In the s-NN method, the different number of nearest neighbors can be selected for each new case. Performance evaluation using the data from UCI Machine Learning Repository shows that the CBR model adopting the s-NN method outperforms the CBR model adopting the traditional k-NN method.

• Journal of KIISE:Databases
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• v.33 no.7
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• pp.741-753
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• 2006

As the total amount of traffic data in network has been growing at an alarming rate, many researches to mine traffic data with the purpose of getting useful information are currently being performed. However, network users' privacy can be compromised during the mining process. In this paper, we propose an efficient and practical privacy preserving sequential pattern mining method on network traffic data. In order to discover frequent sequential patterns without violating privacy, our method uses the N-repository server model and the retention replacement technique. In addition, our method accelerates the overall mining process by maintaining the meta tables so as to quickly determine whether candidate patterns have ever occurred. The various experiments with real network traffic data revealed tile efficiency of the proposed method.

• KIISE Transactions on Computing Practices
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• v.22 no.5
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• pp.235-239
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• 2016

'Commit-bug link', the link between commit history and bug reports, is used for software maintenance and defect prediction in bug tracking systems. Previous studies have shown that the links are automatically detected based on text similarity, time interval, and keyword. Existing approaches depend on the quality of commit history and could thus miss several links. In this paper, we proposed a technique to link commit and bug report using not only messages of commit history, but also the similarity of files in the commit history coupled with bug reports. The experimental results demonstrated the applicability of the suggested approach.

• Tunnel and Underground Space
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• v.18 no.2
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• pp.134-148
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• 2008

The state of in situ stress is an important factor in considering the suitability of a site as a geological repository for nuclear waste. In this study, three-dimensional distinct numerical analysis was conducted to investigate the effect of deformation zones on the state of stress in the Oskarshamn area, which is one of two candidate sites in Sweden. A discontinuum numerical model was constructed by explicitly representing the numerous deformation zones identified from site investigation and far-field tectonic stress was applied in the constructed model. The numerical model successfully captured the variation of measured stress often observed in the rock mass containing large-scale fractures, which shows that numerical analysis can be an effective tool in improving the understanding of the state of stresses. Discrepancies between measured and modelled stress are attributed to the inconsistent quality of measured stress, uncertainty in geological geometry. and input data for fractures.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.127-133
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• 2019

Since frequent item extraction so far requires searching for patterns and traversal for the FP-Tree, it is more likely to store the mining data in a tree and thus CPU time is required for its searching. In order to overcome these drawbacks, in this paper, we provide each item with its location identification of transaction data without relying on conditional FP-Tree and convert transaction data into 2-dimensional position information look-up table, resulting in the facilitation of time and spatial accessibility. We propose an algorithm that considers the mapping scheme between the location of items and items that guarantees the linear time complexity. Experimental results show that the proposed method can reduce many execution time and memory usage based on the data set obtained from the FIMI repository website.

• Journal of the Korea Society of Computer and Information
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• v.22 no.2
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• pp.81-87
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• 2017

Nowadays wine is increasingly enjoyed by a wider range of consumers, and wine certification and quality assessment are key elements in supporting the wine industry to develop new technologies for both wine making and selling processes. There have been many attempts to construct a more methodical approach to the assessment of wines, but most of them rely on objective decision rather than subjective judgement. In this paper, we propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. We used sequential forward selection and decision tree for this purpose. Experiments with the wine quality dataset from the UC Irvine Machine Learning Repository demonstrate the accuracies of 76.7% and 78.7% for red and white wines respectively.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.281-285
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• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.