• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권3호
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• pp.210-214
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• 2002

Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining fer taking into account the users interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies fer categorical attributes and generalization hierarchies of fuzzy linguistic terms fur quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights f3r attributes.

• 한국안광학회지
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• 제6권1호
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• pp.71-76
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• 2001

본 연구는 연령과 난시축 변화 관계를 알아보기 위해 안과적 질환이 없는 굴절이상안 4277안을 대상으로 타각적, 자각적 굴절검사를 실시하였다. 그 결과 전체 굴절이상안 가운데 난시안은 76.5%로 나타났으며, 연령별로는 20대가 32.1%로 가장 높게, 50대 이상에서는 10.3%로 가장 낮게 나타났다. 난시안의 분포는 직난시 51.4%, 도난시 31.7%, 사난시 16.9%로 직난시 비율이 가장 높게 나타났다. 연령별 난시축의 분포에서 직난시 비율은 10대에서 68.3%로 가장 높았으며, 도난시 비율은 50대 이상에서 72.6%로 가장 놓게 나타났다. 30대에서는 직난시와 도난시의 비율이 비슷하였고, 30대를 기준으로 30대 이전에는 직난시의 비율이, 30대 이후에는 도난시의 비율이 높게 나타났다. 연령이 증가할수록 직난시는 감소하는 반면, 도난시는 증가는 경향을 나타냈는데 이는 젊었을 때의 직난시가 나이가 들면서 도난시로 변화되었다고 추정된다. 사난시는 연령 변화에 크게 영향을 받지 않는 것으로 조사되었다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2002년도 추계정기학술대회
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• pp.241-249
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• 2002

Making traditional plan of target marketing based on Association Rule has brought restriction to obtain the target of marketing. This paper is to present Inverse Association Rule as a new association rule for target marketing. Inverse Association Rule does not use information about relation between items that customers purchase like Association Rule, but use information about relation between items that customers do not pruchase. By adding Inverse Association Rule to target marketing, we generate new marketing rule to look for new target of marketing. From new marketing rule, this paper is to show direct marketing about target item and indirect marketing about another item associated with target item to sell target item. The reason is that sales of the item associated with target item have an influence on sales of target item.

• 정보처리학회논문지D
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• 제17D권6호
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• pp.405-414
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• 2010

다양한 업무요소들의 값의 조합에 따라 대상 값이 결정되는 것을 규칙이라고 한다. 업무를 표현한 기업의 정보시스템은 이러한 수많은 규칙들을 포함하는데, 이러한 규칙들을 구현한 서버 시스템을 규칙기반 시스템이라고 한다. 규칙기반 시스템은 규칙 엔진 기법을 사용하거나 직접 데이터베이스를 사용하여 구현된다. 규칙 엔진 기법은 많은 단점을 가지기 때문에 대부분 관계형 데이터베이스를 사용하여 규칙기반 시스템을 구현한다. 업무의 규모가 커지고 복잡하게 될수록 수많은 다양한 경우의 규칙이 존재하게 되므로 시간과 비용이 크게 증가하고, 대량의 저장공간을 요구하게 될 뿐만 아니라 수행속도의 저하 현상도 많이 발생한다. 따라서 본 연구에서는 이러한 수많은 경우의 규칙들을 동일한 효과를 가지는 간소화된 경우의 규칙들로 변환시킬 수 있는 알고리즘을 제안한다. 본 연구의 알고리즘을 가지고 다양한 업무 규칙 데이터에 적용하여 테스트한 결과 데이터 건수를 간소화시킬 수 있음을 입증하였다. 본 연구의 알고리즘을 사용하여 업무 규칙 데이터를 간소화하게 되면 데이터 베이스를 사용하여 구현된 규칙기반 시스템의 성능을 개선할 수 있다.

• The Journal of Asian Finance, Economics and Business
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• 제6권4호
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• pp.297-307
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• 2019

Despite the fact that the implementation of 5% rule is widely recognized to enhance the transparency of capital market and fairness of corporate governance market, a few evidences present information effect of 5% rule. Using 7,088 non-financial firm-year observations listed on the Korea Stock Exchange from 2006 to 2012, we analyze the relation between trading volume and 5% rule disclosure. The results show that the daily and abnormal trading volume is increased when 5% rule disclosure is released. Moreover, the trading volume is significantly increased during cooling period. Specifically, trading volume is significantly greater when one day before cooling period or the expiration day of cooling period. We also find the information effect of firms with stable ownership structure before 5% rule disclosure is relatively smaller than the firms with unstable ownership structure with unstable ownership structure. These results imply that capital market participants use the information from 5% rule disclosure and reflect in their real economic decision.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1149-1157
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• 2017

The satisfiability problem is always a core problem in artificial intelligence (AI). And how to improve the efficiency of algorithms solving the satisfiability problem is widely concerned. Algorithm IER (Improved Extension Rule) is based on extension rule. The number of atoms and the number of clauses affect the efficiency of the algorithm IER. DPLL rules are helpful to reduce these numbers. Then a complete algorithm CIER based on splitting rule and extension rule is proposed in this paper in order to improve the efficiency. At first, the algorithm CIER (Complete Improved Extension Rule) reduces the scale of a clause set with DPLL rules. Then, the clause set is split into a group of small clause sets. In the end, the satisfiability of the clause set is got from these small clause sets'. A strategy MOAMD (maximum occurrences and maximum difference) for the algorithm CIER is given. With this strategy, a better arrangement of atoms could be got. This arrangement could make the number of small clause sets fewer and the scale of these sets smaller. So, the algorithm CIER will be more efficient.

• 경영과학
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• 제24권2호
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• pp.177-196
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• 2007

For better predictions and classifications in customer recommendation, this study proposes an integrative model that efficiently combines the currently-in-use statistical and artificial intelligence models. In particular, by integrating the models such as Association Rule, Frequency Matrix, and Rule Induction, this study suggests an integrative prediction model. Integrated models consist of four models: ASFM model which combines Association Rule(A) and Frequency Matrix(B), ASRI model which combines Association Rule(A) and Rule Induction(C), FMRI model which combines Frequency Matrix(B) and Rule Induction(C), and ASFMRI model which combines Association Rule(A), Frequency Matrix(B), and Rule Induction(C). The data set for the tests is collected from a convenience store G, which is the number one in its brand in S. Korea. This data set contains sales information on customer transactions from September 1, 2005 to December 7, 2005. About 1,000 transactions are selected for a specific item. Using this data set. it suggests an integrated model predicting whether a customer buys or not buys a specific product for target marketing strategy. The performance of integrated model is compared with that of other models. The results from the experiments show that the performance of integrated model is superior to that of all other models such as Association Rule, Frequency Matrix, and Rule Induction.

• 정보처리학회논문지D
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• 제8D권6호
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• pp.823-834
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• 2001

능동 데이터베이스에서의 규칙(rule)은 트리거하는 사건이 감지되면 자동으로 규칙을 트리거하여 데이터베이스의 상태변화에 대응하는 조치를 자동으로 수행한다. 이러한 능동 규칙은 자신을 포함한 서로 다른 규칙을 트리거 할 수 있으므로 종료하지 못하고 연속적으로 실행될 가능성이 있다. 이와 같은 문제는 규칙의 종료 분석을 통해 차단할 수 있으며 규칙의 종료 분석은 규칙의 컴파일 시간에 수행하는 것이 가장 효과적이다. 따라서 이 논문에서는 규칙 종료분석기를 내장한 규칙 컴파일러를 설계하고 그 수행 모델 및 알고리즘을 제안한다. 아울러 제안 모델의 핵심이라 할 수 있는 규칙 종료 분석 알고리즘의 정형화를 통해 제안 모델의 완전성을 검증한다.

• Journal of information and communication convergence engineering
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• 제8권1호
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• pp.89-94
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• 2010

The most important of rule-base system is the knowledge base that determines the power of rule-base system. The important form of this knowledge is how to descript kinds of rules. The Rule-Base System (RBS) has been using in many field that need reflect quickly change of business rules in management system. As far, when develop the Rule-Based System, we must make a rule engine with a general language. There are three disadvantage of in this developed method. First, while there are many data that must be processed in the system, the speed of processing data will become very slow so that we cannot accept it. Second, we cannot change the current system to make it adaptive to changes of business rules as quickly as possible. Third, large data make the rule engine become very complex. Therefore, in this paper, we propose the two important methods of raising efficiency of Rule-Base System. The first method refers to using the Relational database technology to process the rules of the Rule-Base System, the second method refers to a algorithm of according to Quine McCluskey formula compress the rows of rule table. Because the expressive languages of rule are still remaining many problems, we will introduce a new expressive language, which is Rule-Base Data Model short as RBDM in this paper.

• East Asian Economic Review
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• 제22권4호
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• pp.531-555
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• 2018

This article aims to review the monetary policy rule under inflation targeting framework focusing on Mongolia. The empirical analysis estimates the policy reaction function to see if the inflation targeting has been linked with a monetary policy rule emphasizing on inflation stabilization since its adoption in 2007. The study contributes to the literature by examining the linkage between Mongolian monetary policy rule and inflation targeting directly and thoroughly for the first time and also by taking into account a recent progress in the inflation targeting framework toward forward-looking mode. The main findings were: the Mongolian current monetary policy rule under inflation targeting is characterized as inflation-responsive rule with forward-looking manner (one quarter ahead); the inflation responsiveness is, however, weak enough to be pro-cyclical to inflation pressure; and the rule is also responsive to exchange rate due to the "fear of floating", which weakens the policy reaction to inflation and output gap.