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안길승,허선 대한산업공학회 2016 대한산업공학회지 Vol.42 No.4
Recently, transaction data is accumulated everywhere very rapidly. Association analysis methods are usually applied to analyze transaction data, but the methods have several problems. For example, these methods can only consider one-way relations among items and cannot reflect domain knowledge into analysis process. In order to overcome defect of association analysis methods, we suggest a transaction data analysis method based on probabilistic graphical model (PGM) in this study. The method we suggest has several advantages as compared with association analysis methods. For example, this method has a high flexibility, and can give a solution to various probability problems regarding the transaction data with relationships among items.
효과적인 산업재해 분석을 위한 텍스트마이닝 기반의 사고 분류 모형과 온톨로지 개발
안길승,서민지,허선,Ahn, Gilseung,Seo, Minji,Hur, Sun 한국안전학회 2017 한국안전학회지 Vol.32 No.5
Accident analysis is an essential process to make basic data for accident prevention. Most researches depend on survey data and accident statistics to analyze accidents, but these kinds of data are not sufficient for systematic and detailed analysis. We, in this paper, propose an accident classification model that extracts task type, original cause materials, accident type, and the number of deaths from accident reports. The classification model is a support vector machine (SVM) with word occurrence features, and these features are selected based on mutual information. Experiment shows that the proposed model can extract task type, original cause materials, accident type, and the number of deaths with almost 100% accuracy. We also develop an accident ontology to express the information extracted by the classification model. Finally, we illustrate how the proposed classification model and ontology effectively works for the accident analysis. The classification model and ontology are expected to effectively analyze various accidents.
신경망을 이용한 SNS상에서의 정보확산 예측모형: Digg 사례를 중심으로
안길승,서민지,허선,박유진 한국엔터프라이즈아키텍처학회 2016 정보기술아키텍처연구 Vol.13 No.4
In this paper, we suggest a model based on artificial neural network (ANN) to predict theamount of accumulated information diffusion according to time-flow on social network service(SNS). If the number of output variables is only one and the values of the last two input variablesare similar to each other, then the suggested model predicts the value of output variable as thevalue of last input variable. Otherwise, it predicts the value(s) of output variable(s) by means ofANN. The suggested model is applied to real data in Digg, which is a social bookmarking site, and,as a result, the suggested model in this research outperforms, in terms of prediction accuracy,ARIMA model which is a typical time-series analysis model. 본 연구에서는 소셜 네트워크 서비스(social network service, SNS) 상에서 시간 흐름에 따른누적 정보확산량을 예측하기 위한 인공신경망 기반의 모형을 제안한다. 제안 모형은 누적 정보 확산량을 나타내는 출력 변수의 개수가 한 개이고 마지막 두 입력 변수의 값이 유사하면, 출력 변수의 값을 마지막 입력 변수의 값으로 예측하는 규칙 기반의 예측을 수행하며, 그렇지 않은 경우에는 신경망을 이용하여 시간에 따른 누적 정보 확산량을 예측한다. 제안 모형을 대표적인 소셜 북마킹 사이트인디그(Digg) 데이터에 적용한