• Korean Management Science Review
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• v.21 no.3
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• pp.55-69
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• 2004

This study analyzes customer buying-behavior patterns in a department store as time goes on, and predicts moving patterns of its customers. Through them, it suggests in this paper short-term and long-term marketing promotion strategies. RFM techniques are utilized for customer segmentation. Customers are clustered by using the Kohonen's Self Organizing Map as a method of data mining techniques. Then C5.0, a decision tree analysis technique, is used to predict moving patterns of customers. Using real world data, this study evaluates the prediction accuracy of predictive models.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.857-862
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• 2017

The management of the electric power facilities became important in accordance with the industrial development and electric power facilities were influenced by weather. Even if the same kind of electric power facilities is estimated for extracting the time-varying failure rate, the failure rate could be different depending on external effect such as climate. This research will show the data mining modeling of the weather-related outage and influence of weather on the electric power facility with recent data.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.27-43
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• 2005

This paper presents a new hybrid data mining technique using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method increases prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by both methods, and models error patterns from the cases. Based on the error pattern model, the Predictions of two different methods are merged to generate final prediction. The proposed method has been tested using practical 10 data sets. The analysis results show that the performance of proposed method is superior to the existing methods such as artificial neural networks and decision tree induction.

• Korean Management Science Review
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• v.19 no.2
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• pp.205-220
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• 2002

As the business structure of many industries changes under IT progress and internet economy, the customer information has emerged a key factor in setting up the management policy. The customer has come to replace the product as a central figure in business competition. The domestic life insurance market has also experienced the rapid structural changes in IT time. The competition in the insurance industry to maintain the existing membership and to attract the new members gets stronger under such a new business circumstance. Accordingly, it is necessary for an individual insurance company to develop a systematic marketing plan, based on the customer information, to be competitive in the market. Unlike other studies in which customer characteristics are neglected, this study attempts to utilize the customer information by applying the data mining technique, and then suggests an efficient marketing strategy that could prevail in the competitive business environment.

• Journal of Families and Better Life
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• v.27 no.2
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• pp.203-211
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• 2009

This study was conducted to evaluate a forecast model designed to describe a vocational high school for runaway students. The study included 2000 adolescents from the KEEP(Korean Education and Employment Panel). A Data mining decision tree model revealed that: (1) Suicide ideation was a risk factor for running away among smokers. (2) High self-evaluation was a risk factor for running away among individuals that smoked and had no suicide ideation. (3) Drinking was predicted as a risk factor for no smokers, while family life dissatisfaction was predicted as a risk factor among non-smokers that drank. (4) Negative relationship with mother was predicted as a risk factor among non-drinking non-smoking.

• Proceedings of the KIEE Conference
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• summer
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• pp.722-724
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• 2004

Demand estimates in electric power systems have traditionally consisted of time-series analyses over long time periods. The resulting database consisted of huge amounts of data that were then analyzed to create the various coefficients used to forecast power demand. In this research, we take advantage of universally used analysis techniques analysis, but we also use easily available data-mining techniques to analyze patterns of days and special days(holidays, etc.). We then present a new method for estimating and forecasting power flow using decision tree analysis. And because analyzing the relationship between the estimate and power system ceiling Trices currently set by the Korea Power Exchange. We included power system ceiling prices in our estimate coefficients and estimate method.

• Proceedings of the Korea Database Society Conference
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• 2010.06a
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• pp.187-199
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• 2010

Recently, Internet Telephony has become increasingly popular in telecommunication industry. However, previous research on Internet Telephony has focused on analyzing specific Internet Telephony solutions, identifying the Internet Telephony movement itself. The research on prediction models about Internet Telephony adoption has been minimal. The main propose of this study is to develop models for predicting transition intention from using traditional telephones to using Internet Telephony. To do so, this study uses data mining methods to analyze demands in the IT communications market and to provide management strategies for Internet telephony providers. Especially this study uses discriminant analysis, logistic regression, classification tree, and neural nets to develop the prediction models for the Internet Telephony adoption. The models are compared with each other and a superior model is chosen.

• International Journal of Advanced Culture Technology
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• v.5 no.3
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• pp.40-45
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• 2017

This data mining technique was used to extract useful information from percutaneous coronary intervention data obtained from the US public data homepage. The experiment was performed by extracting data on the area, frequency of operation, and the number of deaths. It led us to finding of meaningful correlations, patterns, and trends using various algorithms, pattern techniques, and statistical techniques. In this paper, information is obtained through efficient decision tree and cluster analysis in predicting the incidence of percutaneous coronary intervention and mortality. In the cluster analysis, EM algorithm was used to evaluate the suitability of the algorithm for each situation based on performance tests and verification of results. In the cluster analysis, the experimental data were classified using the EM algorithm, and we evaluated which models are more effective in comparing functions. Using data mining technique, it was identified which areas had effective treatment techniques and which areas were vulnerable, and we can predict the frequency and mortality of percutaneous coronary intervention for heart disease.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.239-246
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• 2003

Statisticians, or data miners, are often requested to assess the importances of input variables in the given supervised learning model. For the purpose, one may rely on separate ad hoc measures depending on modeling types, such as linear regressions, the neural networks or trees. Consequently, the conceptual consistency in input variable importance measures is lacking, so that the measures cannot be directly used in comparing different types of models, which is often done in data mining processes, In this short communication, we propose a unified approach to the importance measurement of input variables. Our method uses sensitivity analysis which begins by perturbing the values of input variables and monitors the output change. Research scope is limited to the models for continuous output, although it is not difficult to extend the method to supervised learning models for categorical outcomes.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.667-669
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• 2005

유비쿼터스 시대를 향하여 나아가는 현대 사회에서 사람들을 위한 추천시스템은 필수 불가결한 요소 중의 하나이다. 추천 시스템 중에서 사용자의 성별, 나이, 직업 등의 인구 통계적 요소를 고려한 시스템이 주를 이루고 있지만 이러한 시스템에는 어느 정도의 한계가 있다. 추천에 있어서 사용자의 기분, 날씨, 온도 등 주변 환경의 상황이 반영되지 않고 있고 학습을 위한 데이터에 대한 신뢰도 또한 문제가 된다. 이러한 문제점을 해결하기 위해 본 논문에서는 상황정보(Context Information)와 공간 데이터 마이닝(Spatial Data Mining) 기법을 이용한 향상된 추천 시스템을 제안한다. 제안하는 시스템에서는 보다 정확한 추천을 위해 첫째, 날씨, 온도, 사용자의 기분 등의 상황정보를 고려하였다. 그리고 사용자의 유사도 측정을 통해 학습 데이터의 신뢰도를 향상시켰으며, 셋째, 의사결정 트리(Decision Tree) 기법을 이용하여 추천의 정확도를 높였다. 실험을 통하여 측정한 결과 제안하는 추천시스템이 기존의 인구 통계적 요소만을 고려한 시스템이나 의사결정 트리만을 이용한 시스템보다 향상된 성능을 보였다.