• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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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.

• Journal of Korean Ophthalmic Optics Society
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• v.6 no.1
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• pp.71-76
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• 2001

In this study, the variation of astigmatism axis according to the age was studied 4227 eyes of healthy eyes. The refractive errors were measured by objective and subjective refraction. The astigmatism among the total eyes was 76.5%. With-the-rule, against-the-rule and oblique astigmatism were 51.4%, 31.7% and 16.9%, respectively. The frequency of with-the-rule was the highest and against-the-rule was the lowest of all the astigmatism in the refractive error. In relation to the age and astigmatism, the with-the-rule was 68.3% in the 10's age group which was the highest among the astigmatism and the against-the-rule was 10.3% in the 50's age group and upward which was the lowest among the astigmatism. The difference in the with-the-rule and against-the-rule ratio was a little in the 30's age group. As they aged, the tendency of the with-the-rule was increased between 10's and 20's age group continually. And the tendency of the against-the rule was increased between 30's and 50's age group and upward. There was a decrease in with-the-rule and an increase in against-the-rule, with the age increase. The oblique astigmatism relationship to age could not be determined.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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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.

• The KIPS Transactions:PartD
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• v.17D no.6
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• pp.405-414
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• 2010

A rule is defined as a case to determine the target values according to combination of various Business factors. The information system is used to represent enterprise's business, which includes and implements the amount of these rules to Rule-Based System. A Rule-Based System can be constructed by using the rules engine method or Relational Database technology. Because the rules engine method has some disadvantages, the Rule-Based System is mostly developed with Relational Database technology. When business scales become larger and more complex, a large number of various rule cases must be operated in system, and processing these rule cases requires additional time, overhead and storage space, and the speed of execution slows down. To solve these problems, we propose a simplification algorithm that converts a large amount of rule cases to simplification rule cases with same effects. The proposed algorithm is applied to hypothetical business rule data and a large number of simplification experiments and tests are conducted. The final results proved that the number of rows can be reduced to some extent. The proposed algorithm can be used to simplify business rule data for improving performance of the Rule-Based System implemented with the Relational Database.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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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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• v.13 no.5
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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.

• Korean Management Science Review
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• v.24 no.2
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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.

• The KIPS Transactions:PartD
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• v.8D no.6
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• pp.823-834
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• 2001

In the active databases, whenever an event occurs, active rules with the matching event specifications are triggered automatically, its action will be executed. Because these rules may in turn trigger other rules including themselves, the set of rules may be executing each other indefinitely. These problem can be solved by rule termination analysis, and it is efficient for the rule termination to execute in compile time of rule. In this paper we not only design rule compiler with rule termination analyzer, but also propose its execution model and algorithm. The completeness of proposed model is verified by algorithm formalization of rule termination analysis.

• Journal of information and communication convergence engineering
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• v.8 no.1
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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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• v.22 no.4
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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.