• The Journal of Information Technology
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• v.9 no.3
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• pp.27-32
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• 2006

If is interested and create rule between it in item that association rules buys, by negative association rules is interested to item that do not buy, it is attempt to do data Maining more effectively. It is difficult that existent methods to find negative association rules find one part of rule, or negative association rules because use more complicated algorithm than algorithm that find association rules. Therefore, this paper presents method to create negative association rules by simpler process using Boolean Analyzer that use dependency between items. And as Boolean Analyzer through an experiment, show that can find negative association rules and more various rule through comparison with other algorithm.

• Journal of Arbitration Studies
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• v.29 no.2
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• pp.133-159
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• 2019

This paper reviews 2019 new arbitration rules of Japan Commercial Arbitration Association (JCAA). JCAA has amended its Commercial Arbitration Rules, and its Administrative Rules for UNCITRAL Arbitration. Also, it has introduced a new Interactive Arbitrations Rules. These new rules take effect from 1 January 2019. First, principal amendments of JCAA Commercial Arbitration Rules are such as arbitrator impartiality, tribunal secretaries, no dissenting opinions, expedited proceedings, arbitrator fees, administrative fees. Second, JCAA's new Interactive Arbitration Rules compel communication from the arbitral tribunal to the Parties and introduce a system of fixed compensation for arbitrators. Third, JCAA's Administrative Rules for UNCITRAL Arbitration are designed to provide the minimum essentials to allow the UNCITRAL Rules to be overseen by an institution. The only significant updates focus on arbitrator remuneration. This paper presents the intent and some implications of JACC's 2019 new rules for Korean Commercial Arbitration Board (KCAB) arbitration rules. Also, it seeks to provide a meaningful discussion and improvement on the facilitating of arbitration system in Korea.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1077-1083
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• 2006

Association rule mining searches for interesting relationships among items in a given database. 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. In this paper we present association rule mining based antecedent variables. We call these rules to antecedent association rules. An antecedent variable is a variable that occurs before the independent variable and the dependent variable.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.177-188
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• 2005

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 threshold measures in association rule; support and confidence and lift. In the case of appling real world to association rules, we have some difficulties in data interpretation because we obtain many rules. In this paper, we develop the model of association rules using latent variables for environmental survey data.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.149-160
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• 2006

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 threshold measures in association rule; support and confidence and lift. In the case of appling real world to association rules, we have some difficulties in data interpretation because we obtain many rules. In this paper, we develop the model of association rules using latent variables for environmental survey data.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.149-154
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• 2003

Credit scoring system (CSS) starts from an analysis of delinquency trend of each individual or industry. This paper conducts a research on credit card delinquency of bank customers as a preliminary step for building effective credit scoring system to prevent excess loan or bad credit status. To serve this purpose, we use association rules that ore generating method. Specifically, we generate sets of rules of customers who are in bad credit status because of delinquency by using association rules. We expect that the sets of rules generated by association rules could act as an estimator of good or bad credit status classifier.

• Journal of Korea Port Economic Association
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• v.18 no.1
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• pp.27-41
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• 2002

This paper deals with the development of a simulation model for the container terminal, which consists of 3 berths, 8 container cranes, and 16 yard blocks with each yard crane and 90 yard trucks in order to evaluate the various operational rules. The proposed operational rules are 3 ship-dispatching rules, 3 berth allocation rules, 2 crane allocation rules, 2 yard allocation rules, and 2 yard truck allocation rules. These rules are simulated using 4 performance measures, such as ship time in the terminal, ship time in the port, the number of ships processed, and the number of containers handled. The simulation result is as follows: 1) there is no difference among 3 ship-dispatching rules, 2) berth allocation rules depend on performance measures, 3) dynamic crane allocation is better than fixed policy, 4) pooling yard allocation is better than short distance yard allocation rules, and 5) fixed yard truck allocation by berth is a little better than pooling policy.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2007.02a
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• pp.61-71
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• 2007

The Association Rules is the most important method in technology of the data mining. This text further study The Association Rules, has analyzed and commented to Apriori algorithm of The Association Rules. Have realized Apriori algorithm base on Visual Basic 6.0, probe into Apriori algorithm application among the digital library, show with experimental data of application of Association Rules in borrow in the data analysis in readers finally.

• Journal of KIISE:Databases
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• v.35 no.2
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• pp.97-111
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• 2008

Commercial data mining tools basically support binary attributes only in mining association rules, that is, they can mine binary association rules only. In general, however. transaction databases contain not only binary attributes but also quantitative attributes. Thus, in this paper we propose a systematic approach to mine quantitative association rules---association rules which contain quantitative attributes---using commercial mining tools. To achieve this goal, we first propose an overall working framework that mines quantitative association rules based on commercial mining tools. The proposed framework consists of two steps: 1) a pre-processing step which converts quantitative attributes into binary attributes and 2) a post-processing step which reconverts binary association rules into quantitative association rules. As the pre-processing step, we present the concept of domain partition, and based on the domain partition, we formally redefine the previous bipartition and multi-partition techniques, which are mean-based or median-based techniques for bipartition, and are equi-width or equi-depth techniques for multi-partition. These previous partition techniques, however, have the problem of not considering distribution characteristics of attribute values. To solve this problem, in this paper we propose an intuitive partition technique, named standard deviation minimization. In our standard deviation minimization, adjacent attributes are included in the same partition if the change of their standard deviations is small, but they are divided into different partitions if the change is large. We also propose the post-processing step that integrates binary association rules and reconverts them into the corresponding quantitative rules. Through extensive experiments, we argue that our framework works correctly, and we show that our standard deviation minimization is superior to other partition techniques. According to these results, we believe that our framework is practically applicable for naive users to mine quantitative association rules using commercial data mining tools.

• Journal of Information Processing Systems
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• v.4 no.1
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• pp.27-32
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• 2008

Minimum support and confidence have been used as criteria for generating association rules in all association rule mining algorithms. These criteria have their natural appeals, such as simplicity; few researchers have suspected the quality of generated rules. In this paper, we examine the rules from a more rigorous point of view by conducting statistical tests. Specifically, we use contingency tables and chi-square test to analyze the data. Experimental results show that one third of the association rules derived based on the support and confidence criteria are not significant, that is, the antecedent and consequent of the rules are not correlated. It indicates that minimum support and minimum confidence do not provide adequate discovery of meaningful associations. The chi-square test can be considered as an enhancement or an alternative solution.