• Journal of Arbitration Studies
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• v.22 no.1
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• pp.3-22
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• 2012

The Korean Commercial Arbitration Board ("KCAB") recently amended its 'International Arbitration Rules' and the 'Arbitration Rules', which became effective on September 1, 2011. Under the amendment, the 'Arbitration Rules' will be referred to as the 'Domestic Arbitration Rules' and in principle apply only to domestic arbitration cases. Accordingly, the amendment removed all of the provisions relating to international arbitration cases. In addition, under the amendment, the 'International Arbitration Rules' will automatically apply to all international arbitration cases unless the parties agree otherwise. The amended 'International Arbitration Rules' establish new expedited procedures for the international arbitration cases before the KCAB. The KCAB has also instituted additional changes related to international arbitration cases including reduction in the filing and administrative fees and appointment of prominent international foreign arbitrators on its panel. The remuneration for arbitrators has also increased to bring them more in line with the fees provided by other leading international arbitration institutions. While several problems remain, these most recent revisions must be seen as a step in the right direction for the KCAB.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.884-888
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• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

• The Journal of Korean Association of Computer Education
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• v.21 no.1
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• pp.51-58
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• 2018

Recently artificial neural networks have shown excellent performance in various fields. However, there is a problem that it is difficult for a person to understand what is the knowledge that artificial neural network trained. One of the methods to solve these problems is an algorithm for extracting rules from trained neural network. In this paper, we extracted rules from artificial neural networks using ordered-attribute search(OAS) algorithm, which is one of the methods of extracting rules, and analyzed result to improve extracted rules. As a result, we have found that the distribution of output values of the hidden layer unit affects the accuracy of rules extracted by using OAS algorithm, and it is suggested that efficient rules can be extracted by binarizing hidden layer output values using hidden unit clarification.

• Journal of Arbitration Studies
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• v.33 no.2
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• pp.109-137
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• 2023

This paper reviews legal framework, characteristics and main contents of the 'Optional Rules for the Arbitration of Disputes Relating to Outer Space Activity' enacted by the Permanent Court of Arbitration (PCA) in 2011. Space activities, which began in the 1950s, are undergoing significant changes according to the international characteristics and development of science and technology. New Space and the space business will be the key factors driving these changes. However, the diversity of disputes caused by New Space space activities and the characteristics of each type of dispute must be considered together. This is because the space business can be maintained and developed by securing the effectiveness of dispute resolution. This paper identifies that the PCA Space Dispute Arbitration Rules have important legislative and policy significance in this respect. Specifically, in this paper, the international space law system, the draft convention of the International Law Association, and the PCA arbitration rules were introduced in an overview of the international dispute settlement system related to space activities. Afterwards, it examines that the systematic structure and some major contents of the PCA Space Dispute Arbitration Rules in detail. Based on this, the paper suggests some points of application of the PCA Arbitration Rules and the legislative policy implications.

• Journal of Digital Contents Society
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• v.2 no.2
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• pp.117-127
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• 2001

Since the number of e-malls is increased by the rapidly Progress of internet, most e-malls are trying to increase customers' interests by providing personalized services. To Provide this service for CRM, the e-mall must use the personalized rules calculated from customer transaction database. The more filtered transaction data are, the more the e-mall services efficiently and exactly to customer's need. The filtered transaction database is necessary to obtain the food personalized rules. In this paper we propose and develope a personalized e-mall with association rules based on view history of excellent customers who have good transaction data. Association rules based on view history of excellent customers reduce the access time and computing costs. The e-mall with them can provide personalized services more efficiently and exactly.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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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.

• 한국데이터정보과학회:학술대회논문집
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• 2004.10a
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• pp.115-124
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• 2004

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 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 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.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.741-746
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• 2004

As electronic commerce progresses, the temporal association rule is developed from partitioned data sets by time to offer personalized services for customer's interest. In this paper, we proposed a temporal association rule with exponential smoothing method that is giving higher weights to recent data than past data. Through simulation and case study, we confirmed that it is more precise than existing temporal association rules but consumes running time.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.939-948
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• 2012

The most widely used data mining technique is to explore association rules. This technique has been used to find the relationship between items in a massive database based on the interestingness measures such as support, confidence, lift, etc. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control.In general, association rule technique generates the rule, 'If A, then B.', whereas negative association rule technique generates the rule, 'If A, then not B.', or 'If not A, then B.'. We can determine whether we promote other products in addition to promote its products only if we add negative association rules to existing association rules. In this paper, we proposed the negatively attributable and pure confidence to overcome the problems faced by negative association rule technique, and then we checked three conditions for interestingness measure. The comparative studies with negative confidence, negatively pure confidence, and negatively attributable and pure confidence are shown by numerical examples. The results show that the negatively attributable and pure confidence is better than negative confidence and negatively pure confidence.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.717-725
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• 2012

Data mining is a method to find useful information for large amounts of data in database. One of the well-studied problems in data mining is exploration for association rules. Association rule mining searches for interesting relationships among items in a given database by support, confidence, and lift. If we use the existing association rules, we can commit some errors by information loss not to consider the size of occurrence frequency. In this paper, we proposed a new association rule thresholds considering the number of possible rules of interest items and compare with existing association rule thresholds by example and real data. As the results, the new association rule thresholds were more useful than existing thresholds.