• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.235-237
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• 2002

motif란 기능적으로 유사한 단백질 군의 아마노산 서열들에 공통적으로 나타나는 일정한 패턴이나 부분서열을 말한다. 본 논문에서는 motif들로 각 단백질의 특성을 표현한 다음, 이것을 기초로 유사성을 비교하여 단백질들을 기능적으로 유사한 여러개의 계층적 군으로 나누는 군집화 방법을 소개하였다. 영역 특성상 확장성과 계층성을 가지는 신경망 GHSOM을 군집화 알고리즘으로 사용하였고, 실제 307 개의 단백질들에 대한 군집화 실험을 통해 그 효과를 확인해보았다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.169-172
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• 2010

본 논문에서는 기존의 ISODATA 알고리즘을 네트워크 공격탐지에 더욱 적합하도록 개선하여 공격을 탐지하는 새로운 방법을 제안한다. 수많은 인터넷상의 트래픽 정보들을 군집화하여 유사도를 비교하는 방법을 통해 공격을 판단한다. 기본적인 절차는 송신자 IP와 Port, 수신자 IP와 Port 정보를 이용하여 송신자와 수신자 사이의 관계를 분석하고 그 특징 값들을 이용하여 개선된 군집화 알고리즘을 이용하여 군집화를 수행한다. 그리고 얻어진 패턴의 특징값을 인공신경망에 학습하여 공격유형을 분류하고 탐지하도록 한다. 기존의 공격탐지 방법과 비교했을 때, 계산양이 적고 속도가 빠르다는 장점이 있으며 제안하는 방법의 우수성을 실험을 통해 증명하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.950-951
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• 2019

시계열 데이터에 대한 군집화 관련 연구는 주로 통계 분석을 통해 이뤄지기 때문에 데이터가 갖는 특성을 완전히 반영하는 데 한계를 갖는다. 본 논문에서는 다변량 데이터에서의 군집화를 위하여 변수별로 시간에 따른 변화와 특징을 추출하기 위한 CNN-GRU(Convolutional Neural Network - Gated Recurrent Unit) 기반의 신경망 모델을 제안한다. CNN을 활용하여 변수별로 갖는 특성을 파악하고자 하였으며, GRU을 통해 전체 시간에 따른 소비 추세를 도출하고자 하였다. 지역별로 업종에 따라 사용된 2년 치의 실제 카드 데이터를 활용하였으며, 유사한 소비 추세를 보이는 지역을 군집화하는데 이를 적용하였다. 결과적으로, 다변량 시계열 데이터를 통해 전체적인 흐름을 반영하여 패턴화했다는 점에서 의의를 갖는다.

• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.151-165
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• 2009

Fuzzy neural network is an integrated model of artificial neural network and fuzzy system and it has been successfully applied in control and forecasting area. Recently ANFIS(Adaptive Network-based Fuzzy Inference System) has been noticed widely among various fuzzy neural network models because of its outstanding accuracy of control and forecasting area. We design a new classification model based on ANFIS and evaluate it in terms of classification accuracy. We identified ANFIS-based classification model has higher classification accuracy compared to existing classification model, C5.0 decision tree model by comparing their experimental results.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.289-291
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• 2005

최근 유전자 칩의 발전으로 다양하고 방대한 양의 유전자 정보를 이용한 정확하고 신뢰성 높은 분류, 군집 및 질병을 예측하는 분석 기법이 증가하고 있다. 하지만 특징적인 유전자를 선택하는 Gene Selection 기법의 종류는 많지가 않으며 주로 통계적인 방법에 의존하여 유전자를 선택하는 기법을 많이 사용하고 있다. 본 논문에서는 유전자 알고리즘과 신경망의 결합을 통한 데이터마이닝을 기반으로 신뢰성 높은 특징적인 유전자를 선택하는 Gene Selection 기법에 대하여 연구을 진행하였다.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.33-40
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• 2003

Data mining plays an important role in a knowledge discovery process and various algorithms of data mining can be selected for the specific purpose. Most of traditional hierachical clustering methode are suitable for processing small data sets, so they difficulties in handling large data sets because of limited resources and insufficient efficiency. In this study we propose a hybrid neural networks clustering technique, called PPC for Pre-Post Clustering that can be applied to large data sets and find unknown patterns. PPC combinds an artificial intelligence method, SOM and a statistical method, hierarchical clustering technique, and clusters data through two processes. In pre-clustering process, PPC digests large data sets using SOM. Then in post-clustering, PPC measures Similarity values according to cohesive distances which show inner features, and adjacent distances which show external distances between clusters. At last PPC clusters large data sets using the simularity values. Experiment with UCI repository data showed that PPC had better cohensive values than the other clustering techniques.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.95-105
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• 2009

Traffic accidents on highways are likely to happen when there is an imbalance in the complex relationships among key elements such as road geometries, driver related factors, and mechanical performances. The Demand-Effort Model (DEM), which evaluates highway safety, can be explained by the imbalance, which occurs when the level of demand of the driver's attention to the road environment exceeds that of the response from the driver. This study suggests a new model that improves the reliability of the current DEM through the reinterpretation on the physiological signals with the help of the Neural Network Model (NNM). The data were collected from 149 subjects, who drove a test vehicle on the Yongdong, Honam, and Seohaean Expressways in Korea. Three important results could be drawn from the recursive tests as follows; (1) Only 5 out of 10 parameters on the physiological signals which are currently used were proven to be meaningful through the Normality Test, Cluster Analysis, and Mann-Whitney Analysis. (2) The revised DEM, which internally uses the NNM, showed more reliable results than existing DEM. Group 1, which is based on the new DEM showed 80.0% of accuracy in measuring the level of driver's efforts, however, that of Group 2 based on the current DEM was 74.3%. (3) Field tests on the Honam Expressway showed lower 'type II error' with the new DEM (40.5%) than the old DEM (58.8%). The DEM is designed as a quick and easy way to determine highway safety prior to the minute road safety audit (RSA) by a professional audit team. Then a new DEM, which is based on the NNM, needs to be considered since it showed higher reliability and lower error.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.163-171
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• 1997

Function approximation from a set of input-output pairs has numerous applications in scientific and engineering areas. Multilayer feedforward neural networks have been proposed as a good approximator of nonlinear function. The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data we employed. When errorneous traning data are employed, the learned mapping can oscillate badly between data points. In this paper we propose a robust BP learning algorithm that is resistant to the errorneous data and is capable of rejecting gross errors during the approximation process, that is stable under small noise perturbation and robust against gross errors.

• Journal of Korea Multimedia Society
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• v.14 no.9
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• pp.1182-1189
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• 2011

We named the system Visual Path Map which visualizes the distribution of clusters according to characteristics and entire process about exercise prescription, and we purpose to visualize a process according to exercise prescription. Visual Path Map visualizes the distribution of clusters according to characteristics, current and object distribution, and changed distribution for prescription. So it visualizes paths from current distribution to object distribution according to prescription. We used SOM in order to express properties along subjects in Visual Path map, and visualized distribution of clusters about physical characteristics, body mass index, and age information of 1,500 ordinary people. Also we visualize practical exercise prescription according to real data of expert of exercise prescription.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.33-54
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• 2010

The World Wide Web is transitioning from being a mere collection of documents that contain useful information toward providing a collection of services that perform useful tasks. The emerging Web service technology has been envisioned as the next technological wave and is expected to play an important role in this recent transformation of the Web. By providing interoperable interface standards for application-to-application communication, Web services can be combined with component based software development to promote application interaction both within and across enterprises. To make Web services for service-oriented computing operational, it is important that Web service repositories not only be well-structured but also provide efficient tools for developers to find reusable Web service components that meet their needs. As the potential of Web services for service-oriented computing is being widely recognized, the demand for effective Web service discovery mechanisms is concomitantly growing. A number of public Web service repositories have been proposed, but the Web service taxonomy generation has not been satisfactorily addressed. Unfortunately, most existing Web service taxonomies are either too rudimentary to be useful or too hard to be maintained. In this paper, we propose a Web service taxonomy generation framework that combines an artificial neural network based clustering techniques with descriptive label generating and leverages the semantics of the XML-based service specification in WSDL documents. We believe that this is one of the first attempts at applying data mining techniques in the Web service discovery domain. We have developed a prototype system based on the proposed framework using an unsupervised artificial neural network and empirically evaluated the proposed approach and tool using real Web service descriptions drawn from operational Web service repositories. We report on some preliminary results demonstrating the efficacy of the proposed approach.