scholarly journals Process of Web Usage Mining to find Interesting Patterns from Web Usage Data

2012 ◽  
Vol 3 (1) ◽  
pp. 144-148 ◽  
Author(s):  
Ketul Patel ◽  
Dr. A. R. Patel
Keyword(s):  
Data Mining ◽  
Web Sites ◽  
Web Mining ◽  
World Wide ◽  
Pattern Analysis ◽  
Web Usage Mining ◽  
Huge Amount ◽  
Site Structure ◽  
Web Usage ◽  
Usage Data

The traffic on World Wide Web is increasing rapidly and huge amount of data is generated due to users’ numerous interactions with web sites. Web Usage Mining is the application of data mining techniques to discover the useful and interesting patterns from web usage data. It supports to know frequently accessed pages, predict user navigation, improve web site structure etc. In order to apply Web Usage Mining, various steps are performed. This paper discusses the process of Web Usage Mining consisting steps: Data Collection, Pre-processing, Pattern Discovery and Pattern Analysis. It has also presented Web Usage Mining applications and some Web Mining software.

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.

scholarly journals Extraction of Knowledge from Web Server Logs Using Web Usage Mining

2019 ◽  
Vol 8 (S3) ◽  
pp. 12-15
Author(s):  
B. Harika ◽  
T. Sudha
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Web Server ◽  
Primary Source ◽  
Web Usage Mining ◽  
Data Mining Techniques ◽  
Web Usage ◽  
Browsing Behavior ◽  
The Web ◽  
Usage Data

Information on internet increases rapidly from day to day and the usage of the web also increases, thus there is the need to discover interesting patterns from web. The process used to extract and mine useful information from web documents by using Data Mining Techniques is called Web Mining. Web Mining is broadly classified in to three types namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this paper our focus is mainly on Web Usage Mining, where we are applying the data mining techniques to analyse and discover interesting knowledge from the Web Usage data. The activities of the user are captured and stored at different levels such as server level, proxy level and user level called as Web Usage Data and the usage data stored at server side is Web Server Log, where it records the browsing behavior of users and their requests based on the user clicks. Web server Log is a primary source to perform Web Usage Mining. This paper also brings in to discussion of various existing pre-processing techniques and analysis of web log files and how clustering is applied to group the users based on the browsing behavior of users on their interested contents.

Methodologies and Techniques of Web Usage Mining

2017 ◽  
pp. 275-296
Author(s):  
T. Venkat Narayana Rao ◽  
D. Hiranmayi
Keyword(s):  
Web Mining ◽  
Pattern Analysis ◽  
Pattern Discovery ◽  
Secondary Data ◽  
Web Usage Mining ◽  
Sequential Patterns ◽  
Useful Knowledge ◽  
Web Usage ◽  
Automatic Discovery ◽  
Collection Data

Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. It is the type of Web mining activity that involves the automatic discovery of out what users are looking for on the Internet. In this chapter methodology of web usage mining explained in detail which are data collection, data preprocessing, knowledge discovery and pattern analysis. The different Web Usage Mining techniques are described, which are used for knowledge and pattern discovery. These are statistical analysis, sequential patterns, classification, association rule mining, clustering, dependency modeling. Pattern analysis is needed to filter out uninterested rules or patterns from the set found in the pattern discovery phase.

Analysis of Click Stream Patterns using Soft Biclustering Approaches

2011 ◽  
Vol 4 (1) ◽  
pp. 53-66 ◽  
Author(s):  
P. K. Nizar Banu ◽  
H. Inbarani
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Web Mining ◽  
Web Usage Mining ◽  
Web Personalization ◽  
Partial Matching ◽  
Web Usage ◽  
Needed Information ◽  
Highly Correlated ◽  
Web Server Logs

As websites increase in complexity, locating needed information becomes a difficult task. Such difficulty is often related to the websites’ design but also ineffective and inefficient navigation processes. Research in web mining addresses this problem by applying techniques from data mining and machine learning to web data and documents. In this study, the authors examine web usage mining, applying data mining techniques to web server logs. Web usage mining has gained much attention as a potential approach to fulfill the requirement of web personalization. In this paper, the authors propose K-means biclustering, rough biclustering and fuzzy biclustering approaches to disclose the duality between users and pages by grouping them in both dimensions simultaneously. The simultaneous clustering of users and pages discovers biclusters that correspond to groups of users that exhibit highly correlated ratings on groups of pages. The results indicate that the fuzzy C-means biclustering algorithm best and is able to detect partial matching of preferences.

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).

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.

Adaptive Mobile Web Browsing Using Web Mining Technologies

2009 ◽  
pp. 198-207
Author(s):  
Wen-Chen Hu ◽  
Yanjun Zuo ◽  
Lei Chen ◽  
Chyuan-Huei Thomas Yang
Keyword(s):  
Web Sites ◽  
Web Mining ◽  
Data Gathering ◽  
Web Usage Mining ◽  
Handheld Devices ◽  
Personal Digital Assistants ◽  
Web Browsing ◽  
Mining System ◽  
Mobile Web ◽  
Web Usage

Using mobile handheld devices such as smart cellular phones and personal digital assistants (PDAs) to browse the mobile Internet is a trend of Web browsing. However, the small screens of handheld devices and slow mobile data transmission make the mobile Web browsing awkward. This research applies Web usage mining technologies to adaptive Web viewing for handheld devices. Web usage mining 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. A Web usage mining system must be able to perform five major functions: (i) usage data gathering, (ii) data preparation, (iii) navigation pattern discovery, (iv) pattern analysis and visualization, and (v) pattern applications. This approach improves the readability and download speed of mobile Web pages.

Analysis of Click Stream Patterns using Soft Biclustering Approaches

2012 ◽  
pp. 212-224
Author(s):  
P. K. Nizar Banu ◽  
H. Inbarani
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Web Mining ◽  
Web Server ◽  
Web Usage Mining ◽  
Web Personalization ◽  
Partial Matching ◽  
Web Usage ◽  
Highly Correlated ◽  
Web Server Logs

As websites increase in complexity, locating needed information becomes a difficult task. Such difficulty is often related to the websites’ design but also ineffective and inefficient navigation processes. Research in web mining addresses this problem by applying techniques from data mining and machine learning to web data and documents. In this study, the authors examine web usage mining, applying data mining techniques to web server logs. Web usage mining has gained much attention as a potential approach to fulfill the requirement of web personalization. In this paper, the authors propose K-means biclustering, rough biclustering and fuzzy biclustering approaches to disclose the duality between users and pages by grouping them in both dimensions simultaneously. The simultaneous clustering of users and pages discovers biclusters that correspond to groups of users that exhibit highly correlated ratings on groups of pages. The results indicate that the fuzzy C-means biclustering algorithm best and is able to detect partial matching of preferences.

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