• 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.

• Proceedings of the Korean Society for Information Management Conference
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• 2001.08a
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• pp.213-218
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• 2001

본 연구는 육군대학 도서관 홈페이지의 웹서버에 저장되어 있는 로그파일을 실험 데이터로 사용하여, 기존 데이터마이닝(data mining)의 기법들 중에서 연관규칙(association rules) 탐사 기법을 적용함으로써, 사용자들의 웹 항행에 대한 순차패턴을 추출하였다. 이를 분석하여 실제 사용자들이 효과적으로 사용할 수 있는 웹사이트 디자인을 제안하고 나아가 대상 웹사이트의 사용편의성을 평가하였다.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.301-314
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• 2001

Data mining refers to a set of techniques for discovering implicit and useful knowledge from large database. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering knowledge from temporal database, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treat problems for discovering temporal pattern from data which are stamped with time points and do not consider problems for discovering knowledge from temporal interval data. For example, there are many examples of temporal interval data that it can discover useful knowledge from. These include patient histories, purchaser histories, web log, and so on. Allen introduces relationships between intervals and operators for reasoning about relations between intervals. We present a new data mining technique that can discover temporal relation rules in temporal interval data by using the Allen's theory. In this paper, we present two new algorithms for discovering algorithm for generating temporal relation rules, discovers rules from temporal interval data. This technique can discover more useful knowledge in compared with conventional data mining techniques.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.141-148
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• 2015

This paper proposes the BDAS(Big Data analysis Scheme) design that extracts the real time shared information from SNS and Web, analyzes the extracted data rapidly for customers, and makes an on-line marketing strategy efficiently. First, the BDAS collects the data shared in SNS and Web. Second, it provides the result of visualization by analyzing the semantics of the collected data as positive or negative. Therefore, because the BDAS ensures an average 90% accuracy in judging the semantics about the shared SNA and Web data, it can judge customer's propensity accurately and be used for on-line marketing strategy efficiently.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.133-140
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• 2011

For both customers and companies, it is very important to analyze online customer reviews, which consist of small documents that include opinions or experiences about products or services, because the customers can get good informations and the companies can establish good marketing strategies. In this paper, we propose the association model for the opinion mining which can analyze customer opinions posted on web. The association model is to modify the association rules mining model in data mining in order to apply efficiently and effectively the association mining techniques to the opinion mining. We designed and implemented the opinion mining systems based on the modified association model and the grouping idea which would enable it to generate significant rules more.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.113-124
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• 2015

Unlike structured data which are gathered and saved in a predefined structure, unstructured text data which are mostly written in natural language have larger applications recently due to the emergence of web 2.0. Text mining is one of the most important big data analysis techniques that extracts meaningful information in the text because it has not only increased in the amount of text data but also human being's emotion is expressed directly. In this study, we used R program, an open source software for statistical analysis, and studied algorithm implementation to conduct analyses (such as Frequency Analysis, Cluster Analysis, Word Cloud, Social Network Analysis). Especially, to focus on our research scope, we used keyword extract method based on a Data Dictionary. By applying in real cases, we could find that R is very useful as a statistical analysis software working on variety of OS and with other languages interface.

• Journal of Digital Contents Society
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• v.4 no.2
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• pp.191-202
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• 2003

This paper presents a design and implementation example of intelligent equipment management system that is constructed to manage high price equipment of digital content department effectively. To support system operation we executed data mining and presented various rules that appear in dat3 mining process based on dat3 of user, equipment and using record. We presented personalization plan of web site to offer user dependent administration policy and dynamic interface using analyzed informatio.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.143-146
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• 2004

With the rapid growth of e-commerce, marketers are able to observe not only purchasing behavior on what and when customers purchased, but also the individual Web usage behavior that affect purchasing. The richness of this information has the potential to provide marketers with an in-depth understanding of customer. Using commonly available Web log data, this paper examines Web usage behaviors at the individual level. By decomposing the buying process into a pattern of visits and purchase conversion at each visit, we can better understand the relationship between Web usage behavior and purchase decision. This allows us to more accurately forecast a shopper's future purchase decision at the site and hence determine the value of individual customers to the siteAccording to our research, not only information seeking behavior but also visiting duration of a customer and participative behavior such as participation in event should be considered as important predicators of purchase decision of customer in a cosmetic internet shopping mall.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.39-51
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• 2002

In this paper, we propose a poll system on the interned. This poll system makes use of APM which are useful in web and PC using for a server. These tools are all free to obtain, so we can construct this system with the minimum computing environment and the minimum cost. We mention merits and demerits about interned survey and propose how to overcome using this system, and utilize web log data to get an additional information of panel. Finally, we suggest extensibilities of internet survey and the proposed system

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.4-6
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• 2001

In this paper, we designed a system that can mine user's traversal patterns from web log files. The system cleans an input data, transactions of a web log file, and finds traversal patterns from the transactions, each of which consists of one user's access pages. The resulting traversal patterns are shown on a web browser, which can be used to analyze the patterns in visual form by a system manager or data miner. We have implemented the system in an IBM personal computer running on Windows 2000 in MS visual C++, and used the MS SQL Server 2000 to store the intermediate files and the traversal patterns which can be easily applied to a system for knowledge discovery in databases.