• Journal of the Korean Data and Information Science Society
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• 제16권1호
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• pp.59-69
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• 2005

Data mining is the method to find useful information for large amounts of data in database It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze Gyeongnam social indicator survey data by 2001 using association rule technique for environment information. Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. We can use to environmental preservation and environmental improvement by association rule outputs

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.456-463
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• 2001

Understanding and adapting to changes of customer behavior in internet shopping mall is an important aspect to survive in continuously changing environment. This paper develops a methodology based on decision tree algorithms to detect changes of customer behavior automatically from customer profiles and sales data at different time snapshots. We first define three types of changes as emerging pattern, unexpected change and the added/perished rule. Then, it is developed similarity and difference measures for rule matching to detect all types of change. Finally, the degree of change is developed to evaluate the amount of change. A Korean internet shopping mall case is evaluated to represent the performance of our methodology. And practical business implications for this methodology are also provided.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.86-91
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• 2004

As many organizations are searching for ways to compete more effectively in today's market environment. Image of Product is become the most important fact to improve their competition. The objectives of this paper are to provide an overview of PDM(Purchasing Decision Factor) and to discuss how to measure it more efficiently. This study develops a conceptual 'relation model of the purchasing decision factor', which identifies only performance based measurement, and proposes Fuzzy Measuring Method which uses the Fuzzy rule based algorithm to adept survey to date sets.

• 전기학회논문지
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• 제58권3호
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• pp.462-466
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• 2009

The power expansion planning is large and capital intensive capacity planning. In the past, the expansion planning was established with the proper supply reliability in order to minimize social cost. However, the planning can't use cost minimizing objective function in the power markets with many market participants. This paper proposed the power expansion planning model using multi-criteria decision rule. This model used multi objective function considering not only cost minimizing but also GENCO's intension. This paper compared proposed model with WASP model in order to verify the result of proposed model.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2004년도 춘계학술발표회
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• pp.313-318
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• 2004

필 댐이나 제방의 침투에 의한 파괴현상을 원심모형실험으로 모사하는 경우, 지금까지는 물을 이용해 파괴시키는 원심모형실험이 실시되고 있었다. 그리고 점성유체를 사용한 원심모형실험도 실시되고 있지만, 상사법칙의 검증이 이루어지지 않은 상태에서의 실험결과에는 의문이 남아있다. 본 연구에서는, 물의 n배 점성용액을 사용해 상사법칙을 성립시켜 Modeling of Models의 방법에 따라 검증하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.263-266
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• 1995

Comtputer Aided Process Planning(CAPP) has been emerged as playing a key role in Computer Integrated Manufactunng(CIM) as the most critical link to integrate CAD and CAM. A modified variant CAPP system based on process planning rule base is developed in this paper. This CAPP system generates process plans automatically according to the GT code data provided as input. In order to execute process planning, various process planning rules are constructed in the form of decision tree and the inference engine that extracts the process plan based on the tree-structured rules are implemented.

• 산업경영시스템학회지
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• 제23권61호
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• pp.115-126
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• 2000

In General, data mining is defined as the knowledge discovery or extracting hidden necessary information from large databases. Its technique can be applied into decision making, prediction, and information analysis through analyzing of relationship and pattern among data. One of the most important works is to find association rules in data mining. Association Rule is mainly being used in basket analysis. In addition, it has been used in the analysis of web-log and user-pattern. This paper provides the application method in the field of marketing through the analysis of data using association rule as a technique of data mining.

• Journal of the Korean Statistical Society
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• 제9권2호
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• pp.135-144
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• 1980

A problem of choosing units for the series system and the 1-out-of-2 system from k available brands is treated from a decision-theoretic points of view. It is assumed that units from each brand have exponentially distributed life lengths, and that the loss functions are inversely proportional to the reliability of the system. For the series system the 'natural' rule is shown to be optimal. For the 1-out-of-2 system, the Bayes rule wrt the natural conjugate prior is derived and teh constants to implement the Bayes rule are given.

• 한국정보과학회논문지:소프트웨어및응용
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• 제30권7_8호
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• pp.659-671
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• 2003

본 논문에서는 결정 목록을 이용해서 단어 의미 중의성을 해결하는 방법을 제안한다. 결정 목록은 하나 이상의 규칙으로 구성되며, 각 규칙에는 신뢰도가 부여되어 있고, 규칙은 불린 함수(=조건, precondition 와 부류(=의미, class)로 구성되어 있다. 분류 대상이 만족하는 불린 함수를 가진 규칙들 중에서 가장 신뢰도가 높은 규칙에 의해서 분류 대상의 부류가 정해진다. 기존 방법에서는 하나의 자질로 하나의 불린 함수를 구성하는 단일 자질 결정 목록을 이용해서 단어 의미 중의성을 해결했다. 이 경우, 자료 부족 문제와 전처리 과정의 오류에 민감하게 반응한다는 문제점이 있다. 본 논문에서는 기존의 단일자질 결정 목록의 문제점을 해결하기 위해서, 하나 이상의 자질로 불린 함수를 구성하는 다중 자질 결정 목록을 제안하고, 다중 자질 결정 목록을 이용하여, 단어 의미 중의성을 해결하는 방법을 기술하고 있다. 단일 자질 결정 목록과 다중 자질 결정 목록을 비교하기 위해서, 1개의 한국어 의미 부착 말뭉치와 5개의 영어 의미 부착 말뭉치를 대상으로 단어 의미 중의성 해결 실험을 했다. 실험 결과 6개의 말뭉치 모두에 서 다중 자질 결정 목록이 단일 자질 결정 목록에 비해서 더 좋은 결과를 나타냈다.

• Journal of the Korean Data and Information Science Society
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• 제16권3호
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• pp.529-538
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• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are association rules, decision tree, clustering, neural network and so on. Association rule mining searches for interesting relationships among items in a riven large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. We analyze Gyeongnam social indicator survey data using association rule technique for environmental information discovery. We can use to environmental preservation and environmental improvement by association rule outputs.