• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권3호
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• pp.200-205
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• 2005

The objectives of this study were to introduce the easiest and most proper applications of datamining in industrial processes. Applying datamining in manufacturing is very different from applying it in marketing. Misapplication of datamining in manufacturing system results in significant problems. Therefore, it is very important to determine the best procedure and technique in advance. In previous studies, related literature has been introduced, but there has not been much description of datamining applications. Research has not often referred to descriptions of particular examples dealing with application problems in manufacturing. In this study, a datamining roadmap was proposed to support datamining applications for industrial processes. The roadmap was classified into three stages, and each stage was categorized into reasonable classes according to the datamining purposed. Each category includes representative techniques for datamining that have been broadly applied over decades. Those techniques differ according to developers and application purposes; however, in this paper, exemplary methods are described. Based on the datamining roadmap, nonexperts can determine procedures and techniques for datamining in their applications.

• 한국정보통신학회논문지
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• 제10권9호
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• pp.1596-1601
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• 2006

고객관리가 기업의 성패를 좌우하는 중요한 화두로 떠오르면서 보다 쉽고 편리하게 고객의 다양한 패턴을 발견하고 예측하기 위 해 많은 기업들이 CRM과 eCRM을 빠르게 도입하고 있다. 과거엔 고객관리가 통계학자들이나 전문적인 통계패키지에 의해 관리되어 왔으나 정보통신 분야의 급격한 발달을 기반으로 통계적 과정을 자동화시킨 데이터마이닝 기법으로 점점 대체되고 있는 추세이다. 이러한 데이터마이닝이 대표적으로 이용되고 있는 분야가 CRM, eCRM이다. 본 논문에서는 A할인점의 고객 데이터와 2004년도 매출 데이터를 기반으로 유전자알고리즘을 이용한 데이터마이닝을 통해 2005년도 우수 고객을 예측하였고 실제 고객 데이터와의 비교를 통해 데이터 마이닝이 eCRM에 얼마나 효과적인지를 입증하였다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 추계정기학술대회:지능형기술과 CRM
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• pp.289-299
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• 2000

This study provides an application of datamining approach to CQI(Continuous Quality Improvement) using the discharge summary. First, we found a process variation in hospital infection rate by SPC (Statistical Process Control) technique. Second, importance of factors influencing hospital infection was inferred through the decision tree analysis which is a classification method in data-mining approach. The most important factor was surgery followed by comorbidity and length of operation. Comorbidity was further divided into age and principal diagnosis and the length of operation was further divided into age and chief complaint. 24 rules of hospital infection were generated by the decision tree analysis. Of these, 9 rules with predictive prover greater than 50% were suggested as guidelines for hospital infection control. The optimum range of target group in hospital infection control were Identified through the information gain summary. Association rule, which is another kind of datamining method, was performed to analyze the relationship between principal diagnosis and comorbidity. The confidence score, which measures the decree of association, between urinary tract infection and causal bacillus was the highest, followed by the score between postoperative wound disruption find postoperative wound infection. This study demonstrated how datamining approach could be used to provide information to support prospective surveillance of hospital infection. The datamining technique can also be applied to various areas fur CQI using other hospital databases.

• 유통과학연구
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• 제8권1호
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• pp.13-19
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• 2010

기업에서는 마케팅 비용대비 효과를 극대화하기 위하여, 고객을 세분한 후, 목표고객을 선별하여 해당 고객에 적절한 캠페인을 실시하고 있다. 특히 고객세분화 방법으로 통계 모형을 비롯하여 데이터마이닝 방법 등 다양한 방법들이 활용되고 있다. 그 중에서도 데이터마이닝은 1990년대 초에 도입되어 다양한 경영 문제를 해결하고 있다. 본 논문에서는 이와 같은 고객세분화에 활용되고 있는 데이터마이닝 방법에 대해 살펴본 후, 실제 백화점 사례를 기반으로 고객세분화에 주로 활용되고 있는 의사결정나무 분석 방법의 효과 및 장단점에 대해 논의해보고자 한다.

• 한국정보통신학회논문지
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• 제9권7호
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• pp.1582-1587
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• 2005

본 논문은 정수장에서 사용하는 응집제의 종류를 결정하기 위한 시스템 개발에 관한 내용이다. 정수장은 여러 단위 처리장으로 구성되며, 불순물을 제거하기 위하여 혼화지에서 응집제를 주입하여 침전을 시킨다. 현재까지 응집제 결정을 위해 Jar-test를 이용하는데, 이 방법은 사람의 주관적인 판단에 의존하므로 실험 오차가 발생할 수 있다. 특히 정수장의 자동화를 위한 시스템 개발에서 가장 큰 걸림돌로 작용하고 있다. 본 논문은 이러한 문제점을 해결하기 위하여 로드맵에 기초한 데이터마이닝 기법을 이용하여 응집제를 선택할 수 있는 제어기를 개발하였다. 제어 규칙은 클러스터링 기법으로 도출하였는데, 군집의 초기 값과 개수는 통계적 지수 값을 사용하여 결정하였다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2007년도 한국지능정보시스템학회
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• pp.329-337
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• 2007

Case-based reasoning (CBR) methods are applied to various target problems on the supposition that previous cases are sufficiently similar to current target problems, and the results of previous similar cases support the same result consistently. However, these assumptions are not applicable for some target cases. There are some target cases that have no sufficiently similar cases, or if they have, the results of these previous cases are inconsistent. That is, the appropriateness of CBR is different for each target case, even though they are problems in the same domain. Thus, applying CBR to whole datasets in a domain is not reasonable. This paper presents a new hybrid datamining technique called two-step filtering CBR and Rule Induction (TSFCR), which dynamically selects either CBR or RI for each target case, taking into consideration similarities and consistencies of previous cases. We apply this method to three medical diagnosis datasets and one credit analysis dataset in order to demonstrate that TSFCR outperforms the genuine CBR and RI.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2005년도 춘계 학술발표회 논문집
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• pp.317-324
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• 2005

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 datamining technique. This paper treats an evolutionary computation method based on genetic programming (GP), which can be one of the components to realize datamining.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권3호
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• pp.179-183
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• 2006

ECG consists of various waveforms of electric signals of heat. Datamining can be used for analyzing and classifying the waveforms. Conventional studies classifying electrocardiogram have problems like extraction of distorted characteristics, overfitting, etc. This study classifies electrocardiograms by using BP algorithm and SVM to solve the problems. As results, this study finds that SVM provides an effective prohibition of overfitting in neural networks and guarantees a sole global solution, showing excellence in generalization performance.

• Journal of Information Technology Applications and Management
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• 제9권4호
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• pp.65-78
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• 2002

We propose a new application method of the datamining technique that might help building an efficient trade strategy in the stock market, where the analysis of the huge database is essential. The proposed method utilizes the association rules among the price changes of individual stock from the market basket analysis (a datamining technique typically used in the Marketing field) in building the strategy We also apply the proposed method to the daily stock prices in Korean stock market, from Jan. 2000 to Dec. 2001. The application results show that the proposed method gives an significantly higher yield rate than the actual stock chage rate.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 추계학술대회 논문요약집
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• pp.147-147
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• 2003