• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.1869-1880
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• 2009

Distribution line fault has been reduced gradually by the efforts on improving the quality of electrical materials and distribution system maintenance. However faults caused by medium voltage customers have been increased gradually even though we have done many efforts. The problem is that we don't know which customer will cause the fault. This paper presents the concept to find these customers using data mining techniques, which is based on accumulated fault records of medium voltage customers in the past. It also suggests the prediction model construction of medium voltage customers causing distribution line fault and methods to enhance the reliability of distribution system. We expect that we can effectively reduce faults resulted from medium voltage customers, which is 30% of total faults.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.7-8
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• 2018

This study proposes an analysis of the Word2vec-based machine learning classifiers for the sake of opinion mining tasks. As a bench-marking method, BOW (Bag-of-Words) was adopted. On the basis of utilizing the Word2vec and BOW as feature extraction methods, we applied Laptop and Restaurant dataset to LR, DT, SVM, RF classifiers. The results showed that the Word2vec feature extraction yields more improved performance.

• The KIPS Transactions:PartD
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• v.18D no.3
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• pp.169-178
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• 2011

Recently, data sharing between enterprises or organizations is required matter for task cooperation. In this process, when the enterprise opens its database to the affiliates, it can be occurred to problem leaked sensitive information. To resolve this problem it is needed to hide sensitive information from the database. Previous research hiding sensitive information applied different heuristic algorithms to maintain quality of the database. But there have been few studies analyzing the effects on the items modified during the hiding process and trying to minimize the hided items. This paper suggests eFP-Tree(Extended Frequent Pattern Tree) based FP-Tree(Frequent Pattern Tree) to hide sensitive frequent itemsets. Node formation of eFP-Tree uses border to minimize impacts of non sensitive frequent itemsets in hiding process, by organizing all transaction, sensitive and border information differently to before. As a result to apply eFP-Tree to the example transaction database, the lost items were less than 10%, proving it is more effective than the existing algorithm and maintain the quality of database to the optimal.

• The KIPS Transactions:PartD
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• v.17D no.4
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• pp.253-258
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• 2010

Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.

• Korea Trade Review
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• v.46 no.5
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• pp.65-81
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• 2021

The technological development in the era of the 4th industrial revolution is changing the paradigm of various industries. Various technologies such as big data, cloud, artificial intelligence, virtual reality, and the Internet of Things are used, creating synergy effects with existing industries, creating radical development and value creation. Among them, the logistics sector has been greatly influenced by quantitative data from the past and has been continuously accumulating and managing data, so it is highly likely to be linked with big data analysis and has a high utilization effect. The modern advanced technology has developed together with the data mining technology to discover hidden patterns and new correlations in such big data, and through this, meaningful results are being derived. Therefore, data mining occupies an important part in big data analysis, and this study tried to analyze data mining techniques that can contribute to the logistics field and common logistics using these data mining technologies. Therefore, by using the AHP technique, it was attempted to derive priorities for each type of efficient data mining for logisticalization, and R program and R Studio were used as tools to analyze this. Criteria of AHP method set association analysis, cluster analysis, decision tree method, artificial neural network method, web mining, and opinion mining. For the alternatives, common transport and delivery, common logistics center, common logistics information system, and common logistics partnership were set as factors.

• Journal of Korea Water Resources Association
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• v.51 no.11
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• pp.999-1009
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• 2018

A Model Tree technique of data mining was applied to derive optimal equations for sediment discharge assessment based on the measured sediment data and then to evaluate stable channel design for Naesung Stream. The sediment discharge formula as a function of channel width, velocity, depth, slope and median grain diameter which was developed by a Model Tree technique with sediment discharge data measured in Korean River had a high goodness-of-fit between measured and calculated results. In case of the sediment discharge formula as a function of channel width, velocity, depth and median grain diameter which was developed by a Model Tree technique with sediment discharge data only measured in Naesung Stream represented the highest goodness-of-fit. Two types of sediment discharge formulas were applied to evaluate stable channel analysis for Yonghyeol Station of Naesung Stream. As a result, bed erosion was expected in the study section compared to the current section. It was also presented that the be slope might be changed to be a milder slope than the current slope to reach equilibrium condition in the long term.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.602-602
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• 2006

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2C
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• pp.208-218
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• 2006

In this paper, we introduce the creation methods of attack detection model using data mining technologies that can classify the latest attack types, and can detect the modification of existing attacks as well as the novel attacks. Also, we evaluate comparatively these attack detection models in the view of detection accuracy and detection time. As the important factors for creating detection models, there are data, attribute, and detection algorithm. Thus, we used NetFlow data gathered at the real network, and KDD Cup 1999 data for the experiment in large quantities. And for attribute selection, we used a heuristic method and a theoretical method using decision tree algorithm. We evaluate comparatively detection models using a single supervised/unsupervised data mining approach and a combined supervised data mining approach. As a result, although a combined supervised data mining approach required more modeling time, it had better detection rate. All models using data mining techniques could detect the attacks within 1 second, thus these approaches could prove the real-time detection. Also, our experimental results for anomaly detection showed that our approaches provided the detection possibility for novel attack, and especially SOM model provided the additional information about existing attack that is similar to novel attack.

• Geomechanics and Engineering
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• v.23 no.1
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• pp.51-59
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• 2020

In this paper, the WEKA platform was used to mine and analyze measured data of floor failure depth and a prediction system of floor failure depth was developed with Java. Based on the standardization and discretization of 35-set measured data of floor failure depth in China, the grey correlation degree analysis on five factors affecting the floor failure depth was carried out. The correlation order from big to small is: mining depth, working face length, floor failure resistance, mining thickness, dip angle of coal seams. Naive Bayes model, neural network model and decision tree model were used for learning and training, and the accuracy of the confusion matrix, detailed accuracy and node error rate were analyzed. Finally, artificial neural network was concluded to be the optimal model. Based on Java language, a prediction system of floor failure depth was developed. With the easy operation in the system, the prediction from measured data and error analyses were performed for nine sets of data. The results show that the WEKA prediction formula has the smallest relative error and the best prediction effect. Besides, the applicability of WEKA prediction formula was analyzed. The results show that WEKA prediction has a better applicability under the coal seam mining depth of 110 m~550 m, dip angle of coal seams of 0°~15° and working face length of 30 m~135 m.