• Journal of the military operations research society of Korea
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• v.34 no.3
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• pp.93-105
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• 2008

Recently, mental patients are increasing by secondary gain, that is, purpose for avoiding a military service as the social prejudice about mental disease decreases. In particular, it is unique diagnostic tool to a patient's subjective symptoms complaint and a doctor's special judgment, in occasion of psychiatry problem that an objective diagnostic tool does not exist. In this paper, we provide an objective basis to help in a quick decision-making of discharging from military service using the datamining, that analyzes mental patient's prescription to find a special rule. Therefore, we propose the decision support system of discharging from service using the analysis of mental patient's prescription.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.473-476
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• 2003

Nowaday, Internet is doing the role of a large distributed information service tenter and various information and database servers managing it are in distributed network environment. However, the we have several difficulties in deciding the server to disposal input data depending on data properties. In this paper, we designed server mining mechanism and Intellectual data mining system architecture for the best efficiently dealing with input data pattern by using neural network among the various data in distributed environment. As a result, the new input data pattern could be operated after deciding the destination server according to dynamic binding method implemented by neural network. This mechanism can be applied Datawarehous, telecommunication and load pattern analysis, population census analysis and medical data analysis.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.527-540
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• 2010

Rule-based classification analysis is widely used for massive datamining because it is easy to understand and its algorithm is uncomplicated. In this classification analysis, majority vote of rules or weighted combination of rules using their supports are frequently used in order to combine rules. We propose a method to combine rules by using the multinomial distribution in this paper. Iterative proportional fitting algorithm is used to estimate the multinomial distribution which maximizes entropy constrained on rules' support. Simulation experiments show that this method can compete with other well known classification models in the case of two similar populations.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.577-590
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• 2016

We studied the current state-of-the-art of Smart TV, the challenges and the drawbacks. Mainly we discussed the lack of end-to-end solution. We then illustrated the differences between Smart TV and IPTV from network service provider point of view. Unlike IPTV, viewer of Smart TV's over-the-top (OTT) services could be global, such as foreign nationals in a country or viewers having special viewing preferences. Those viewers are sparsely distributed. The existing TV service deployment models over Internet are not suitable for such viewers as they are based on content popularity, hence we propose a community based service deployment methodology with proactive content caching on rendezvous points (RPs). In our proposal, RPs are intermediate nodes responsible for caching routing and decision making. The viewer's community formation is based on geographical locations and similarity of their interests. The idea of using context information to do proactive caching is itself not new, but we combined this with "in network caching" mechanism of content centric network (CCN) architecture. We gauge the performance improvement achieved by a community model. The result shows that when the total numbers of requests are same; our model can have significantly better performance, especially for sparsely distributed communities.

• Convergence Security Journal
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• v.5 no.2
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• pp.1-8
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• 2005

Network event analysis gives useful information on the network status that helps protect attacks. It involves finding sets of frequently used packet information such as IP addresses and requires real-time processing by its nature. Apriori algorithm used for data mining can be applied to find frequent item sets, but is not suitable for analyzing network events on real-time due to the high usage of CPU and memory and thus low processing speed. This paper develops a network event audit module by applying association rules to network events using a new algorithm instead of Apriori algorithm. Test results show that the application of the new algorithm gives drastically low usage of both CPU and memory for network event analysis compared with existing Apriori algorithm.

• Journal of Korea Game Society
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• v.6 no.4
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• pp.79-87
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• 2006

This paper proposes the design and implementation of engine to control characters by using machine teaming techniques. Because the proposed engine uses the context data in the rum time as the knowledge data, there is a merit which the player can not easily recognize the behavior pattern of the intelligent character. To do this, the paper proposes to develop the module which gathers and trains the context data and the module which tests to decide the optimal context control for the given context data. The developed engine is ported to FEAR and run with Quake2 and experimented far the correctness of the development and its efficiency. The experiments show that the developed engine is operated well and efficiently within the limited time.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.5
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• pp.25-32
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• 2004

An anti-fraud system that utilizes association rules of fraud as well as AFS (Anti Fraud System) for credit card payments in e-commerce is proposed. The association rules are found by applying the data mining algorithm to millions of transaction records that have been generated as a result of orders on goods through the Internet. When a customer begins to process an order by using transaction components of a secure messaging protocol, the degree of risk for the transaction is assessed by using the found rules. More credit information will be requested or the transaction is rejected if it is interpreted as risky.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1089-1098
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• 2007

Currently, user require is diverse on the Web. Furthermore, each web user is wishing to retrieve data or goods that hey want to look for more conveniently and more quickly. Because different search criteria and dispositions of web users, they lead to unnecessary repeated operations in order to use implemented by web designer. In this paper, we suggest the system that analyzes user patterns on the Web using the technique of log file analysis and transfers more effectively the information of web sites to users. And we analyze the log file for customer data in the system the proposed method are implemented by means of EC-Miner that is one of the tool of datamining, and aims to offer appropriate Layout corresponding with personalization by giving weight to each transport path.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.5 s.143
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• pp.534-541
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• 2005

Until now, Korean shipyards have accumulated a great amount of data. But they do not have appropriate tools to utilize the data in practical works. Engineering data contains experts' experience and know-how in its own. It is very useful to extract knowledge or information from the accumulated existing data by using data mining technique This paper treats an evolutionary computation based on genetic programming (GP), which can be one of the components to realize data mining. The paper deals with linear models of GP for the regression or approximation problem when given learning samples are not sufficient. The linear model, which is a function of unknown parameters, is built through extracting all possible base functions from the standard GP tree by utilizing the symbolic processing algorithm. In addition to a standard linear model consisting of mathematic functions, one variant form of a linear model, which can be built using low order Taylor series and can be converted into the standard form of a polynomial, is considered in this paper. The suggested model can be utilized as a designing tool to predict design parameters with small accumulated data.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.275-278
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• 2008

Recently, there are many research of datamining. On the transaction dataset, association rules is made by finding of interesting patterns. A part of mining, sub-structure mining is increased in interest of and applied to many high technology. But graph mining has more computing time then itemset mining. Therefore, that need efficient way for avoid duplication. GASTON is best algorithm of duplication free. This paper analyze GASTON and expect the future work.