• The Journal of Korean Association of Computer Education
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• v.6 no.4
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• pp.171-180
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• 2003

The recent paradigm of web-based teaching-learning is changing into a direction that analyzes the learning patterns of learners on the basis of learners' ability, aptitude, request, interest, learning history, activity profile, etc. and provides adaptive environment with individual learners The present study analyzed learners' learning patterns using data on learning activities and developed a personalization system that provides learning environment adapted to individual learners. This study customized in three aspects, which are recommendation of learning path, recommendation of interface and recommendation of interaction, through Web mining. The personalization system developed in this study was proved to be effective in improving individual learners' satisfaction with learning in Web-based teaching-learning environment.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.647-656
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• 2003

In Webspace, mining traversal patterns is to understand user's path traversal patterns. On this mining, it has a unique characteristic which objects (for example, URLs) may be visited due to their positions rather than contents, because users move to other objects according to providing information services. As a consequence, it becomes very complex to extract meaningful information from these data. Recently discovering traversal patterns has been an important problem in data mining because there has been an increasing amount of research activity on various aspects of improving the quality of information services. This paper presents a Dynamic Link Recommendation (DLR) algorithm that recommends link sets on a Web site through mining frequent traversal patterns. It can be employed to any Web site with massive amounts of data. Our experimentation with two real Weblog data clearly validate that our method outperforms traditional method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.115-126
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• 2000

In General, data mining is defined as the knowledge discovery or extracting 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 works is to find association rules in data mining. Association Rule is mainly being used in basket analysis. In addition, it has been used in the analysis of web-log and user-pattern. This paper provides the application method in the field of marketing through the analysis of data using association rule as a technique of data mining.

• The Journal of Society for e-Business Studies
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• v.5 no.2
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• pp.113-125
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• 2000

There are many web-based applications which need graphs and charts to be generated from data stored in the database. This paper does a comparative study on graph processing techniques for web-based applications through a case study of building a stock information system. The result of this paper can be used for building effective web applications with graphs in areas of EC(electronic commerce), EIS(executive information system), and DM(data mining).

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.135-148
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• 2003

In this paper we introduce the Internet-based purchase support systems using data mining and case-based reasoning (CBR). Internet Business activity that involves the end user is undergoing a significant revolution. The ability to track users browsing behavior has brought the vendor and end customer's closer than ever before. It is now possible for a vendor to personalize his product message for individual customers at massive scale. Most of former researchers, in this research arena, used data mining techniques to pursue the customer's future behavior and to improve the frequency of repurchase. The area of data mining can be defined as efficiently discovering association rules from large collections of data. However, the basic association rule-based data mining technique was not flexible. If there were no inference rules to track the customer's future behavior, association rule-based data mining systems may not present more information. To resolve this problem, we combined association rule-based data mining with CBR mechanism. CBR is used in reasoning for customer's preference searching and training through the cases. Data mining and CBR-based hybrid purchase support mechanism can reflect both association rule-based logical inference and case-based information reuse. A Web-log data gathered in the real-world Internet shopping mall is given to illustrate the quality of the proposed systems.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.247-249
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• 2001

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.35-63
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• 2001

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1537-1542
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• 2004

Various studies to increase customer satisfaction of a web based system are performed actively. Also in recent days an interest about the personalization that supporting a order type service on customer's viewpoint was raised. So the studies supporting the personalization is required in a web-based marketing system. In this study, we designed an intelligent recommendation system which supporting one to one web marketing using cross selling. The proposed system used an intelligent data mining method as a concurrent cross selling and a sequential cross selling. Also, In experiment on the prototype, we show a proposed system was usable in an practical system applying the mining result.

• Journal of Information Technology Applications and Management
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• v.17 no.2
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• pp.71-90
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• 2010

Electronic learning(or e-learning) has created hype for companies, universities, and other educational institutions. It has led to the phenomenal growth in the use of web-based learning and experimentation with multimedia, video conferencing, and internet-based technologies. Many researchers are interested in the factors that affect to the performance of e-learning or e-learning services. In this sense, this study is aimed at proposing e-learning system reuse prediction models in which e-learner intention to reuse influence factors(i.e., system accessibility, system stability, information clarity, information validity, self-regulated efficacy, computer self-efficacy, perceived usefulness, perceived ease of use, flow, and parental expectation) affect e-learner intention to reuse positively. A web survey was conducted for the full members of the e-learning education institute A in Seoul, Republic of Korea, an exclusive e-learning company that provides real time video lectures via the desktop conferencing system. The web survey was conducted for 20 days from November 5, 2009, through the e-learning web site of the company A. In this study, three data mining techniques were used : the multivariate discriminant analysis, CART, and C5.0 algorithm. This study was conducted to provide the e-learning service providers, e-learning operators, and contents developers with marketing and management strategies for improving the e-learning service companies, based on the data mining analysis results.

• IE interfaces
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• v.18 no.2
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• pp.205-220
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• 2005

This study presents a web log analysis model for e-CRM, which combines the on-line customer's purchasing pattern data and transaction data between companies in B2B environment of make-to-order company. With this study, the customer evaluation and the customer subdivision are available. We can forecast the estimate demands with periodical products sales records. Also, the purchasing rate per each product, the purchasing intention rate, and the purchasing rate per companies can be used as the basic data for the strategy for receiving the orders in future. These measures are used to evaluate the business strategy, the quality ability on products, the customer's demands, the benefits of customer and the customer's loyalty. And it is used to evaluate the customer's purchasing patterns, the response analysis, the customer's secession rate, the earning rate, and the customer's needs. With this, we can satisfy various customers' demands, therefore, we can multiply the company's benefits. And we presents case of the 'H' company, which has the make-to-order manufacture environment, in order to verify the effect of the proposal system.