• Title/Summary/Keyword: Web data mining

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Fuzzy Web Usage Mining for User Modeling

  • Jang, Jae-Sung;Jun, Sung-Hae;Oh, Kyung-Whan
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.2 no.3
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    • pp.204-209
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    • 2002
  • The interest of data mining in artificial intelligence with fuzzy logic has been increased. Data mining is a process of extracting desirable knowledge and interesting pattern ken large data set. Because of expansion of WWW, web data is more and more huge. Besides mining web contents and web structures, another important task for web mining is web usage mining which mines web log data to discover user access pattern. The goal of web usage mining in this paper is to find interesting user pattern in the web with user feedback. It is very important to find user's characteristic fer e-business environment. In Customer Relationship Management, recommending product and sending e-mail to user by extracted users characteristics are needed. Using our method, we extract user profile from the result of web usage mining. In this research, we concentrate on finding association rules and verify validity of them. The proposed procedure can integrate fuzzy set concept and association rule. Fuzzy association rule uses given server log file and performs several preprocessing tasks. Extracted transaction files are used to find rules by fuzzy web usage mining. To verify the validity of user's feedback, the web log data from our laboratory web server.

Hybrid Intelligent Web Recommendation Systems Based on Web Data Mining and Case-Based Reasoning

  • Kim, Jin-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.3
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    • pp.366-370
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    • 2003
  • In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers' preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can't present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

A Study of Web Usage Mining for eCRM

  • Hyuncheol Kang;Jung, Byoung-Cheol
    • Communications for Statistical Applications and Methods
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    • v.8 no.3
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    • pp.831-840
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    • 2001
  • In this study, We introduce the process of web usage mining, which has lately attracted considerable attention with the fast diffusion of world wide web, and explain the web log data, which Is the main subject of web usage mining. Also, we illustrate some real examples of analysis for web log data and look into practical application of web usage mining for eCRM.

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An Efficient Approach for Single-Pass Mining of Web Traversal Sequences (단일 스캔을 통한 웹 방문 패턴의 탐색 기법)

  • Kim, Nak-Min;Jeong, Byeong-Soo;Ahmed, Chowdhury Farhan
    • Journal of KIISE:Databases
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    • v.37 no.5
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    • pp.221-227
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    • 2010
  • Web access sequence mining can discover the frequently accessed web pages pursued by users. Utility-based web access sequence mining handles non-binary occurrences of web pages and extracts more useful knowledge from web logs. However, the existing utility-based web access sequence mining approach considers web access sequences from the very beginning of web logs and therefore it is not suitable for mining data streams where the volume of data is huge and unbounded. At the same time, it cannot find the recent change of knowledge in data streams adaptively. The existing approach has many other limitations such as considering only forward references of web access sequences, suffers in the level-wise candidate generation-and-test methodology, needs several database scans, etc. In this paper, we propose a new approach for high utility web access sequence mining over data streams with a sliding window method. Our approach can not only handle large-scale data but also efficiently discover the recently generated information from data streams. Moreover, it can solve the other limitations of the existing algorithm over data streams. Extensive performance analyses show that our approach is very efficient and outperforms the existing algorithm.

Research on Data Acquisition Strategy and Its Application in Web Usage Mining (웹 사용 마이닝에서의 데이터 수집 전략과 그 응용에 관한 연구)

  • Ran, Cong-Lin;Joung, Suck-Tae
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.12 no.3
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    • pp.231-241
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    • 2019
  • Web Usage Mining (WUM) is one part of Web mining and also the application of data mining technique. Web mining technology is used to identify and analyze user's access patterns by using web server log data generated by web users when users access web site. So first of all, it is important that the data should be acquired in a reasonable way before applying data mining techniques to discover user access patterns from web log. The main task of data acquisition is to efficiently obtain users' detailed click behavior in the process of users' visiting Web site. This paper mainly focuses on data acquisition stage before the first stage of web usage mining data process with activities like data acquisition strategy and field extraction algorithm. Field extraction algorithm performs the process of separating fields from the single line of the log files, and they are also well used in practical application for a large amount of user data.

Web Mining for successful e-Business based on Artificial Intelligence Techniques (성공적인 e-Business를 위한 인공지능 기법 기반 웹 마이닝)

  • 이장희;유성진;박상찬
    • Journal of Intelligence and Information Systems
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    • v.8 no.2
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    • pp.159-175
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    • 2002
  • Web mining is an emerging science of applying modem data mining technologies to the problem of extracting valid, comprehensible, and actionable information from large databases of web in e-Business environment and of using it to make crucial e-Business decisions. In this paper, we present the noble framework of data visualization system based on web mining for analyzing the characteristics of on-line customers in e-Business. We also propose the framework of forecasting system for providing the forecasting information of sales/purchase through the use of web mining based on artificial intelligence techniques such as back-propagation network, memory-based reasoning, and self-organizing map.

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A New Approach to Web Data Mining Based on Cloud Computing

  • Zhu, Wenzheng;Lee, Changhoon
    • Journal of Computing Science and Engineering
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    • v.8 no.4
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    • pp.181-186
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    • 2014
  • Web data mining aims at discovering useful knowledge from various Web resources. There is a growing trend among companies, organizations, and individuals alike of gathering information through Web data mining to utilize that information in their best interest. In science, cloud computing is a synonym for distributed computing over a network; cloud computing relies on the sharing of resources to achieve coherence and economies of scale, similar to a utility over a network, and means the ability to run a program or application on many connected computers at the same time. In this paper, we propose a new system framework based on the Hadoop platform to realize the collection of useful information of Web resources. The system framework is based on the Map/Reduce programming model of cloud computing. We propose a new data mining algorithm to be used in this system framework. Finally, we prove the feasibility of this approach by simulation experiment.

A Framework for Web Log Analysis Using Process Mining Techniques (프로세스 마이닝을 이용한 웹 로그 분석 프레임워크)

  • Ahn, Yunha;Oh, Kyuhyup;Kim, Sang-Kuk;Jung, Jae-Yoon
    • Journal of Information Technology and Architecture
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    • v.11 no.1
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    • pp.25-32
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    • 2014
  • Web mining techniques are often used to discover useful patterns from data log generated by Web servers for the purpose of web usage analysis. Yet traditional Web mining techniques do not reflect sufficiently sequential properties of Web log data. To address such weakness, we introduce a framework for analyzing Web access log data by using process mining techniques. To illustrate the proposed framework, we show the analysis of Web access log in a campus information system based on the framework and discuss the implication of the analysis result.

A Clustering Algorithm Considering Structural Relationships of Web Contents

  • Kang Hyuncheol;Han Sang-Tae;Sun Young-Su
    • Communications for Statistical Applications and Methods
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    • v.12 no.1
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    • pp.191-197
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    • 2005
  • Application of data mining techniques to the world wide web, referred to as web mining, has been the focus of several recent researches. With the explosive growth of information sources available on the world wide web, it has become increasingly necessary to track and analyze their usage patterns. In this study, we introduce a process of pre-processing and cluster analysis on web log data and suggest a distance measure considering the structural relationships between web contents. Also, we illustrate some real examples of cluster analysis for web log data and look into practical application of web usage mining for eCRM.

User Access Patterns Discovery based on Apriori Algorithm under Web Logs (웹 로그에서의 Apriori 알고리즘 기반 사용자 액세스 패턴 발견)

  • Ran, Cong-Lin;Joung, Suck-Tae
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.12 no.6
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    • pp.681-689
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    • 2019
  • Web usage pattern discovery is an advanced means by using web log data, and it's also a specific application of data mining technology in Web log data mining. In education Data Mining (DM) is the application of Data Mining techniques to educational data (such as Web logs of University, e-learning, adaptive hypermedia and intelligent tutoring systems, etc.), and so, its objective is to analyze these types of data in order to resolve educational research issues. In this paper, the Web log data of a university are used as the research object of data mining. With using the database OLAP technology the Web log data are preprocessed into the data format that can be used for data mining, and the processing results are stored into the MSSQL. At the same time the basic data statistics and analysis are completed based on the processed Web log records. In addition, we introduced the Apriori Algorithm of Web usage pattern mining and its implementation process, developed the Apriori Algorithm program in Python development environment, then gave the performance of the Apriori Algorithm and realized the mining of Web user access pattern. The results have important theoretical significance for the application of the patterns in the development of teaching systems. The next research is to explore the improvement of the Apriori Algorithm in the distributed computing environment.