• 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 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.

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 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.

Web Navigation Mining by Integrating Web Usage Data and Hyperlink Structures (웹 사용 데이타와 하이퍼링크 구조를 통합한 웹 네비게이션 마이닝)

  • Gu Heummo;Choi Joongmin
    • Journal of KIISE:Software and Applications
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    • v.32 no.5
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    • pp.416-427
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    • 2005
  • Web navigation mining is a method of discovering Web navigation patterns by analyzing the Web access log data. However, it is admitted that the log data contains noisy information that leads to the incorrect recognition of user navigation path on the Web's hyperlink structure. As a result, previous Web navigation mining systems that exploited solely the log data have not shown good performance in discovering correct Web navigation patterns efficiently, mainly due to the complex pre-processing procedure. To resolve this problem, this paper proposes a technique of amalgamating the Web's hyperlink structure information with the Web access log data to discover navigation patterns correctly and efficiently. Our implemented Web navigation mining system called SPMiner produces a WebTree from the hyperlink structure of a Web site that is used trl eliminate the possible noises in the Web log data caused by the user's abnormal navigational activities. SPMiner remarkably reduces the pre-processing overhead by using the structure of the Web, and as a result, it could analyze the user's search patterns efficiently.

Development of Active Data Mining Component for Web Database Applications (웹 데이터베이스 응용을 위한 액티브데이터마이닝 컴포넌트 개발)

  • Choi, Yong-Goo
    • Journal of Information Technology Applications and Management
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    • v.15 no.2
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    • pp.1-14
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    • 2008
  • The distinguished prosperity of information technologies from great progress of e-business during the last decade has unavoidably made software development for active data mining to discovery hidden predictive information regarding business trends and behavior from vary large databases. Therefore this paper develops an active mining object(ADMO) component, which provides real-time predictive information from web databases. The ADMO component is to extended ADO(ActiveX Data Object) component to active data mining component based on COM(Component Object Model) for application program interface(API). ADMO component development made use of window script component(WSC) based on XML(eXtensible Markup Language). For the purpose of investigating the application environments and the practical schemes of the ADMO component, experiments for diverse practical applications were performed in this paper. As a result, ADMO component confirmed that it could effectively extract the analytic information of classification and aggregation from vary large databases for Web services.

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