• 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

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.945-954
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• 2002

Clickstream is the information which demonstrate users' path through web sites. Analysis of clickstream shows how web sites are navigated and used by users. Clickstream of online web sites contains effective information of web marketing and to offers usefully personalized services to users, and helps us understand how users find web sites, what products they see, and what products they purchase. In this paper, we present an extended web log system that add to module of collection of clickstream to understand users' behavior patterns In web sites. This system offers the users clickstream information to database which can then analyze it with ease. Using ADO technology in store of database constructs extended web log server system. The process of making clickstreaming into database can facilitate analysis of various user patterns and generates aggregate profiles to offer personalized web service. In particular, our results indicate that by using the users' clickstream. We can achieve effective personalization of web sites.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.95-105
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• 2009

There are various studies to provide useful information for users on huge data of web-sites. Web usage mining among them is a method to extract meaningful patterns based on web users' log data. Most of existing patterns of web usage mining, however, had not considered users' diverse inclination but created general models. Web users' keywords can have various meaning upon their tendency and background knowledge. This study is for generating Multi Concept Keyword Model (MCK-Model) by analyzing web usage information on users' keywords of interest. MCK-Model can supply web page network for various inclination based on users' keywords of interest. Also, MCK-Model can be used to recommend the most proper web pages and it has been confirmed that the suggested method is useful enough.

• Educational Technology International
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• v.15 no.2
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• pp.71-88
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• 2014

A variety of studies to predict students' performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students' learning behaviors. However, it is still challenging to predict students' learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students' academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.313-315
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• 2004

인터넷 사용이 보편화됨에 따라 기존의 방화벽만으로는 탐지가 불가능한 웹 서비스의 취약점을 이용한 공격이 증가하고 있다. 그 중에서도 특히 웹 어플리케이션의 프로그래밍 오류를 이용한 침입이 공격 수단의 대부분을 차지하고 있다. 본 논문에서는 웹 어플리케이션의 동작을 분석한 후 취약점 발생 부분에 대해 웹 로그 마이닝 기법을 사용하여 실시간으로 로그를 분석함으로서 공격 패턴을 비교ㆍ분석한다. 또한 프로세스 분석기를 통한 결정(decision) 과정을 통해 침입으로 판단되면 해당 접속 프로세스(pid)를 제거 한 후 공격 아이피를 차단함으로서 침입을 탐지하는 메커니즘을 제시한다.

• KIISE Transactions on Computing Practices
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• v.22 no.12
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• pp.646-653
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• 2016

Outbound market is a rapidly growing global industry, and has evolved into a 11 trillion won trade. A lot of recommender systems, which are based on collaborative and content filtering, target the existing purchase log or rely on studies based on similarity of products. These researches are not highly efficient as data was not obtained in advance, and acquiring the overwhelming amount of data has been relatively slow. The characteristics of an outbound product are that it should be purchased at least twice in a year, and its pricing should be in the higher category. Since the repetitive purchase of a product is rare for the outbound market, the old recommender system which profiles the existing customers is lacking, and has some limitations. Therefore, due to the scarcity of data, we suggest an improved customer-profiling method using web usage mining, algorithm of association rule, and rule-based algorithm, for faster recommender system of outbound product.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.19-25
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• 2020

In frequent pattern mining, the uncertainty of each item is accompanied by a loss of information. AAlso, in real environment, the importance of patterns changes with time, so fuzzy logic must be applied to meet these requirements and the dynamic characteristics of the importance of patterns should be considered. In this paper, we propose a fuzzy utility mining technique for extracting frequent web page sets from web log databases through fuzzy utility-based web page set mining. Here, the downward closure characteristic of the fuzzy set is applied to remove a large space by the minimum fuzzy utility threshold (MFUT)and the user-defined percentile(UDP). Extensive performance analyses show that our algorithm is very efficient and scalable for Fuzzy Utility Mining using dynamic weights.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.190-192
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• 2002

데이터 마이닝(Data Mining)이란 저장된 많은 양의 자료로부터 통계적 수학적 분석방법을 이용하여 다양한 가치 있는 정보를 찾아내는 일련의 과정이다. 데이터 클러스터링은 이러한 데이터 마이닝을 위한 하나의 중요한 기법이다. 본 논문에서는 Fuzzy C-Means 알고리즘을 이용하여 웹 사용자들의 행위가 기록되어 있는 웹 로그 데이터를 데이터 클러스터링 하는 방법에 관하여 연구하고자 한다. Fuzzv C-Means 클러스터링 알고리즘은 각 데이터와 각 클러스터 중심과의 거리를 고려한 유사도 측정에 기초한 목적 함수의 최적화 방식을 사용한다. 웹 로그 데이터의 여러 필드 중에서 사용자 IP, 시간, 웹 페이지 필드를 WLDF(Web Log Data for FCM)으로 가공한 후, 다차원 Fuzzy C-Means 클러스터링을 한다. 그리고 이를 이용하여 샘플 데이터와 임의의 데이터간의 유사 패턴 분석을 하고자 한다.

• 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.