• The Journal of Society for e-Business Studies
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• v.4 no.1
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• pp.21-36
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• 1999

Using user monitoring techniques on web, marketing decision makers in cyber shopping malls can gather customer behavior data as well as sales transaction data and customer profiles. In this paper, we present a marketing rule extraction technique for customer behavior analysis in cyber shopping malls, The technique is an application of market basket analysis which is a representative data mining technique for extracting association rules. The market basket analysis technique is applied on a customer behavior log table, which provide association rules about web pages in a cyber shopping mall. The extracted association rules can be used for mall layout design, product packaging, web page link design, and product recommendation. A prototype cyber shopping mall with customer monitoring features and a customer behavior analysis algorithm is implemented using Java Web Server, Servlet, JDBC(Java Database Connectivity), and relational database on windows NT.

• Journal of Arbitration Studies
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• v.20 no.2
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• pp.67-90
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• 2010

Electronic discovery refers to the discovery of electronically stored information. The differences between producing paper documents and electronic information can be categorized into seven groups: massive volume, persistence, dynamic and changeable contents, metadata, environment-dependence, dispersion and searchability. Since these differences make the discovery more expensive and less expeditious, it is necessary to limit the scope of discovery. Accordingly, a number of arbitration institutions have already introduced rules, guidelines or protocols on electronic discovery. ICDR guidelines take a minimal approach and address only the proper form of electronic document. CIArb Protocol is intended to act as a checklist for discovery of electronic data. CPR Protocol offers four modes of discovery of electronic documents ranging from minimal to extensive among which the parties may choose the way of electronic discovery. IBA Rules on Evidence and ICC Rules are silent on the issue of electronic discovery, however, working parties of the ICC are considering updates to the rules to deal with electronic discovery. It is disputed whether rules, guidelines or protocols on electronic discovery is necessary or appropriate. Although some have suggested that existing rules can make adequate provision for electronic discovery, it is more desirable to prepare new rules, guidelines or protocols to make arbitrators and counsels be familiar with electronic discovery process, to provide an adequate standard for electronic discovery and to limit the time and cost of electronic discovery. Such rules on electronic discovery should include provisions regarding the form of electronic document production, conference between parties regarding electronic discovery, keyword search, bearing the expenses to reduce disputes over electronic discovery.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.16 no.1
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• pp.11-16
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• 2006

There are many counting rules for analyzing man-made mineral fibers. The representatives are the NIOSH Method 7400 A and B counting rules. The two rules have different rules of length-to-width ratio(aspect ratio) and diameter. The A rule counts only fibers $>5{\mu}m$ in length, and only fibers with aspect ratio >3:1. The B rule counts only ends of fibers $>5{\mu}m$ in length and $<3{\mu}m$ in diameter, and only fibers with aspect ratio ${\geq}5:1$. The A counting rule had been used before the B counting rule was introduced. The purpose of this study is to compare the A and B counting rules for airborne fibers from various man-made mineral fibers(glass wool fibers, rock wool fibers, refractory ceramic fibers, and continuous filament glass fibers) industries. There were significantly differences between the paired counts of A and B rules in all types of fibers(p<0.05). A rule counts/B rule counts(A/B ratios) were 1.52 for glass fibers, 1.53 for rock wool fibers, 1.19 for RCF, and 1.82 for continuous filament glass fibers. The counting results by A and B counting rules were highly correlated in glass wool fibers, rock wool fibers and refractory ceramic fibers(RCF) samples (r=0.96 for all types of fibers) except continuous filament glass fibers(r=0.82). Regression equations to correct for the differences between counting rules were presented in this paper.

• Journal of Arbitration Studies
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• v.16 no.1
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• pp.121-151
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• 2006

As globalisation extends its effect and particularly following China's accession to the World Trade Organization(WTO) in 2001, ever greater numbers of international transactions will feature a Chinese party. China has certainly made efforts in recent years to rectify law problem. While conducting business in China, foreign companies occasionally find themselves embroiled in disputes with Chinese individuals and companies. As foreign businesses invest in the extraordinary market opportunities in China, international arbitration has also become the preferred method for handling disputes with Chinese partners or with other foreign corporation over operations in China. The new Arbitration Rules of the International Economic and Trade Arbitration Commission(CIETAC) came into force on 1 May 2005. The new rules represent a major overhaul of CIETAC arbitration procedures and are sure to enhance CIETAC's position as a leading player in the resolution of China-foreign business disputes. The changes are significant for all companies doing business in China. So, this article investigated some amendments on the basis of 2000 Rules.

• Korean Journal of Hydrosciences
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• v.4
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• pp.65-79
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• 1993

Selection of the best operating rule among a set of alternatives for a multipurpose reservoir system operation requires to evaluate many minor criteria in addition to the major objectives assessed to the system. These problems are sufficiently complex and difficult that they are beyond heuristic decision rules and experiences in case several noncommensurable multiple criteria are included in the evaluation. With the assistance of multicriterion decision analysis techniques, it is possible to select the best one among various alternatives by systematically comparing and ranking the alternatives with respect to the criteria of choice. Evaluation criteria for multipurpose reservoir system operating rules were identified and defined, and the multicriterion decision analysis techniques were applied to evaluate the four existing operating rules of the Chungju multipurpose project according to the identified nine multiple criteria. The application results show that the methodology is very efficient to select the best operation alternative among a finite number of operating rules with many evaluation criteria for a large-scale reservoir system operation.

• Journal of Arbitration Studies
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• v.8 no.1
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• pp.353-375
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• 1998

This study is focused on the rules for resolving documentary credits disputes. First, International Chamber of Commerce published Documentary Credit Dispute Expertise Rules on October 1, 1997. The DOCDEX Rules are the International Chamber of Commerce(ICC) response to a clear call from the international banking community for a rapid, cost effective, expert-based dispute resolution mechanism for documentary credit practice, including bank-to-bank reimbursement issues. Next, The International Center for Letter of Credit Arbitration was established in September 1996. The Center was founded as a result of an initiative from within the letter of credit community and has been co-sponsored by the United States Council on International Banking(USCIB) and the Institute of International Banking Law and Practice Inc. In September, ICLOCA adopted its "Rules of Arbitration for Letter of Credit Disputes." Therefore, parties to letter of credit disputes should choose a appropriate dispute resolution mechanism under the circumstances in the future.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Proceedings of the Safety Management and Science Conference
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• 2000.11a
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• pp.299-306
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• 2000

General definition of data mining is the knowledge discovery or is to extract 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 work is to find association rules in data mining. The objective of this paper is to find customer's trend using association rule from analysis of database and the result can be used as fundamental data for CRM(Customer Relationship Management). This paper uses Apriori algorithm and FoodMart data in order to find association rules.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.195-209
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• 2003

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, but use information about relation between items that customers do not purchase. By adding inverse association rule to target marketing, we generate new marketing strategy to look for new target of marketing. There are three steps to apply the marketing strategy proposed by this Paper to target marketing. Firstly, a database is converted to an inverse database. Although inverse association rules can be generated from a database, it is easier to explain inverse association rule in an inverse database than in a database. Secondly, association rules and inverse association rules are generated from inverse database. Finally, two types of rules which are created in the previous steps are applied to target 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.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.59-66
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• 2010

We present a personalized clothing recommender system - one that mines association rules from transaction described in ontologies and infers a recommendation from the rules. The recommender system can forecast frequently changing trends of clothing using the Onto-Apriori algorithm, and it makes appropriate recommendations for each users possible through the inference marked as meta nodes. We simulates the rule generator and the inferential search engine of the system with focus on accuracy and efficiency, and our results validate the system.