World Wide Web Usage Mining

2012 ◽  
pp. 367-396
Author(s):  
Ajith Abraham
Keyword(s):  
World Wide Web ◽  
World Wide ◽  
Web Usage Mining ◽  
Web Usage

World Wide Web Usage Mining

2011 ◽  
pp. 1242-1248 ◽  
Author(s):  
Wen-Chen Hu ◽  
Hung-Jen Yang ◽  
Chung-wei Lee ◽  
Jyh-haw Yeh
Keyword(s):  
Data Mining ◽  
World Wide Web ◽  
World Wide ◽  
Data Gathering ◽  
Web Usage Mining ◽  
Web Data ◽  
Mining System ◽  
Data Mining Techniques ◽  
Web Usage ◽  
Content Mining

World Wide Web data mining includes content mining, hyperlink structure mining, and usage mining. All three approaches attempt to extract knowledge from the Web, produce some useful results from the knowledge extracted, and apply the results to certain real-world problems. The first two apply the data mining techniques to Web page contents and hyperlink structures, respectively. The third approach, Web usage mining (the theme of this article), is the application of data mining techniques to the usage logs of large Web data repositories in order to produce results that can be applied to many practical subjects, such as improving Web sites/pages, making additional topic or product recommendations, user/customer behavior studies, and so forth. This article provides a survey and analysis of current Web usage mining technologies and systems. A Web usage mining system must be able to perform five major functions: (i) data gathering, (ii) data preparation, (iii) navigation pattern discovery, (iv) pattern analysis and visualization, and (v) pattern applications. Many Web usage mining technologies have been proposed, and each technology employs a different approach. This article first describes a generalized Web usage mining system, which includes five individual functions. Each system function is then explained and analyzed in detail. Related surveys of Web usage mining techniques also can be found in Hu, et al. (2003) and Kosala and Blockeel (2000).

scholarly journals Ekstraksi Click Stream Data Web E-Commerce Menggunakan Web Usage Mining

2021 ◽  
Vol 7 (2) ◽  
pp. 65-72
Author(s):  
Kartina Diah Kusuma Wardani
Keyword(s):  
World Wide Web ◽  
Association Rules ◽  
World Wide ◽  
Web Usage Mining ◽  
Frequent Itemset ◽  
Sequence Mining ◽  
Stream Data ◽  
Log Data ◽  
Web Usage

E-Commerce berkembang pesat dalam world wide web hingga menghasilkan berbagai jenis data yang dapat dianalisa lebih lanjut untuk berbagai keperluan seperti personifikasi web, profiling customer, dan sebagainya. Salah satu jenis data yang dihasilkan e-Commerce adalah click stream data web yang merekam aktivitas visitor web dalam bentuk log data selama berinteraksi pada laman web. Penelitian ini mengekstraksi click stream data web e-commerce untuk mendapatkan pola interaksi konsumen terhadap halaman web selama mengunjungi web e-commerce. Berdasarkan jenis data yang diekstrak maka web usage mining digunakan untuk ekstraksi pola dari click stream data yang berbentuk log data. Teknik mining yang dianalisa terhadap log data e-commerce pada penelitian ini terdiri dari frequent itemset, asociation rules, dan frequence sequence mining. Frequent itemset menghasilkan halaman web yang paling sering diakses oleh visitor. Association rules menghasilkan pola kemungkinan halaman web yang akan diakses visitor jika visitor mengakses halaman-halamn tertentu. Frequence sequence mining mendapatkan pola urutan halaman web yang paling sering diakses oleh visitor web e-commerce saat berinteraksi pada laman web. Pola urutan halaman yang diakses visitor menunjukkan urutan kebiasaan visitor mengunjungi e-commerce. Sedangkan teknik mining yang diimplementasikan untuk menghasilkan pola akses visitor pada penelitian ini adalah Frequence sequence mining. Hasil ekstraksi dari penelitian ini menunjukkan ada enam halaman web yang paling sering diakses oleh konsumen dengan berbagai pola urutan aksesnya.

Web Usage Mining and Its Applications

2011 ◽  
pp. 1221-1225
Author(s):  
Yongjian Fu
Keyword(s):  
Data Mining ◽  
World Wide ◽  
Online Shopping ◽  
Rapid Development ◽  
User Feedback ◽  
Web Usage Mining ◽  
Web Based ◽  
Web Usage ◽  
The World ◽  
The Web

With the rapid development of the World Wide Web or the Web, many organizations now put their information on the Web and provide Web-based services such as online shopping, user feedback, technical support, and so on. Understanding Web usage through data mining techniques is recognized as an important area.

Web Usage Mining

Web Mining ◽  
2011 ◽  
pp. 373-392 ◽  
Author(s):  
Yew-Kwong Woon ◽  
Wee-Keong Ng ◽  
Ee-Peng Lim
Keyword(s):  
Data Mining ◽  
World Wide ◽  
Web Usage Mining ◽  
Web Usage ◽  
Online Business ◽  
Web Access ◽  
Business Competitiveness ◽  
Access Logs ◽  
Web Server Logs ◽  
Web Access Logs

The rising popularity of electronic commerce makes data mining an indispensable technology for several applications, especially online business competitiveness. The World Wide Web provides abundant raw data in the form of Web access logs. However, without data mining techniques, it is difficult to make any sense out of such massive data. In this chapter, we focus on the mining of Web access logs, commonly known as Web usage mining. We analyze algorithms for preprocessing and extracting knowledge from such logs. We will also propose our own techniques to mine the logs in a more holistic manner. Experiments conducted on real Web server logs verify the practicality as well as the efficiency of the proposed techniques as compared to an existing technique. Finally, challenges in Web usage mining are discussed.

Classifying Web Usage Behavior in the Workplace

2011 ◽  
pp. 211-234
Author(s):  
Murugan Anandarajan
Keyword(s):  
United States ◽  
World Wide Web ◽  
World Wide ◽  
The United States ◽  
The Internet ◽  
Web Usage ◽  
The World ◽  
International Data ◽  
Data Group ◽  
The Web

The ubiquitous nature of the World Wide Web (commonly known as the Web) is dramatically revolutionizing the manner in which organizations and individuals alike acquire and distribute information. Recent reports from the International Data Group indicate that the number of people on the Internet will reach 320 million by the year 2002 (Needle, 1999). Studies also indicate that in the United States alone, Web commerce will account for approximately $325 billion by the year 2002.

scholarly journals Mapping an audience-centric World Wide Web: A departure from hyperlink analysis

2016 ◽  
Vol 19 (9) ◽  
pp. 1331-1348 ◽  
Author(s):  
Harsh Taneja
Keyword(s):  
World Wide Web ◽  
World Wide ◽  
Cultural Factors ◽  
The Other ◽  
Web Usage ◽  
Hyperlink Analysis

This article argues that maps of the Web’s structure based solely on technical infrastructure such as hyperlinks may bear little resemblance to maps based on Web usage, as cultural factors drive the latter to a larger extent. To test this thesis, the study constructs two network maps of 1000 globally most popular Web domains, one based on hyperlinks and the other using an “audience-centric” approach with ties based on shared audience traffic between these domains. Analyses of the two networks reveal that unlike the centralized structure of the hyperlink network with few dominant “core” Websites, the audience network is more decentralized and clustered to a larger extent along geo-linguistic lines.

scholarly journals AN INDISCERNIBILITY APPROACH FOR PRE PROCESSING OF WEB LOG FILES

2012 ◽  
pp. 231-234
Author(s):  
JEEVA JOSE ◽  
P. SOJAN LAL
Keyword(s):  
World Wide Web ◽  
Set Theory ◽  
Rough Set ◽  
World Wide ◽  
Rough Set Theory ◽  
Web Usage Mining ◽  
Web Log ◽  
Log Files ◽  
Challenging Tasks ◽  
And Behavior

World Wide Web has a spectacular growth not only in terms of the number of websites and volume of information, but also in terms of the number of visitors. Web log files contain tremendous information about the user traffic and behavior. A large amount of pre processing is required for eliminating the noise and is one of the challenging tasks in web usage mining. This paper proposes an indiscernibility approach in rough set theory for pre processing of web log files.

Applications of Web Usage Mining across Industries

2017 ◽  
pp. 92-115
Author(s):  
A. V. Senthil Kumar ◽  
R. Umagandhi
Keyword(s):  
Web Sites ◽  
Web Mining ◽  
World Wide ◽  
Research Area ◽  
Web Usage Mining ◽  
General Knowledge ◽  
Main Research ◽  
Web Usage ◽  
Research Activities ◽  
Log File

Web Usage Mining (WUM) is the process of discovery and analysis of useful information from the World Wide Web (WWW) by applying data mining techniques. The main research area in Web mining is focused on learning about Web users and their interactions with Web sites by analysing the log entries from the user log file. The motive of mining is to find users' access models automatically and quickly from the vast Web log data, such as similar queries imposed by the various users, frequent queries applied by the user, frequent web sites visited by the users, clustering of users with similar intent etc. This chapter deals with Web mining, Categories of Web mining, Web usage mining and its process, Applications of Web usage mining across the industries and its related works. This Chapter offers a general knowledge about Web usage mining and its applications for the benefits of researchers those performing research activities in WUM.

Applications of Web Usage Mining across Industries

2017 ◽  
pp. 2005-2029
Author(s):  
A. V. Senthil Kumar ◽  
R. Umagandhi
Keyword(s):  
Web Sites ◽  
Web Mining ◽  
World Wide ◽  
Research Area ◽  
Web Usage Mining ◽  
General Knowledge ◽  
Main Research ◽  
Web Usage ◽  
Research Activities ◽  
Log File

Web Usage Mining (WUM) is the process of discovery and analysis of useful information from the World Wide Web (WWW) by applying data mining techniques. The main research area in Web mining is focused on learning about Web users and their interactions with Web sites by analysing the log entries from the user log file. The motive of mining is to find users' access models automatically and quickly from the vast Web log data, such as similar queries imposed by the various users, frequent queries applied by the user, frequent web sites visited by the users, clustering of users with similar intent etc. This chapter deals with Web mining, Categories of Web mining, Web usage mining and its process, Applications of Web usage mining across the industries and its related works. This Chapter offers a general knowledge about Web usage mining and its applications for the benefits of researchers those performing research activities in WUM.

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