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.

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.

Extracting Knowledge from Web Data

2014 ◽  
Vol 7 (4) ◽  
pp. 27-41
Author(s):  
Hanane Ezzikouri ◽  
Mohamed Fakir ◽  
Cherki Daoui ◽  
Mohamed Erritali
Keyword(s):  
Web Mining ◽  
User Behavior ◽  
Data Extraction ◽  
Research Area ◽  
Web Usage Mining ◽  
Web Data ◽  
Main Research ◽  
Web Log ◽  
Web Usage ◽  
Log File

The user behavior on a website triggers a sequence of queries that have a result which is the display of certain pages. The Information about these queries (including the names of the resources requested and responses from the Web server) are stored in a text file called a log file. Analysis of server log file can provide significant and useful information. Web Mining is the extraction of interesting and potentially useful patterns and implicit information from artifacts or activity related to the World Wide Web. Web usage mining is a main research area in Web mining focused on learning about Web users and their interactions with Web sites. The motive of mining is to find users' access models automatically and quickly from the vast Web log file, such as frequent access paths, frequent access page groups and user clustering. Through Web Usage Mining, several information left by user access can be mined which will provide foundation for decision making of organizations, Also the process of Web mining was defined as the set of techniques designed to explore, process and analyze large masses of consecutive information activities on the Internet, has three main steps: data preprocessing, extraction of reasons of the use and the interpretation of results. This paper will start with the presentation of different formats of web log files, then it will present the different preprocessing method that have been used, and finally it presents a system for “Web content and Usage Mining'' for web data extraction and web site analysis using Data Mining Algorithms Apriori, FPGrowth, K-Means, KNN, and ID3.

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.

scholarly journals Requisite for Web Usage Mining – A Survey

2013 ◽  
pp. 67-73
Author(s):  
Sindhu P Menon ◽  
Nagaratna P Hegde
Keyword(s):  
Web Sites ◽  
Web Mining ◽  
World Wide ◽  
Research Area ◽  
Important Research ◽  
The Past ◽  
The World ◽  
Important Research Area ◽  
Indispensable Tool ◽  
The Web

The World Wide Web (Web) has been providing an important and indispensable platform for receiving information and disseminating information as well as interacting with society on the Internet. With its astronomical growth over the past decade, the Web becomes huge, diverse and dynamic. The application of data mining techniques to the web is called Web Mining. Web Mining aims to discover interesting patterns in the structure, the contents and the usage of web sites. An indispensable tool for the webmaster, it has, nevertheless, a long road ahead in which visualisation plays an important role. Currently, Web mining techniques has emerged as an important research area to help Web users find the information needed. This paper is an effort in analysing the views and methodologies stated by various authors on various processes in mining the web.

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.

Building User Communities of Interests by Using Latent Semantic Analysis

2011 ◽  
pp. 38-68
Author(s):  
Guandong Xu
Keyword(s):  
Latent Semantic Analysis ◽  
Web Mining ◽  
Semantic Analysis ◽  
Information Overload ◽  
Critical Issue ◽  
Web Usage Mining ◽  
Semantic Level ◽  
Web Usage ◽  
User Communities ◽  
Usage Patterns

Nowadays Web users are facing the problems of information overload and drowning due to the significant and rapid growth in the amount of information and the large number of users. As a result, how to provide Web users more exactly needed information is becoming a critical issue in Web-based information retrieval and data management. In order to address the above difficulties, Web mining was proposed as an efficient means to discover the intrinsic relationships among Web data. In particular, Web usage mining is to discover Web usage patterns and utilize the discovered usage knowledge for constructing interest-oriented user communities, which could be, in turn, used for presenting Web users more personalized Web contents, i.e. Web recommendation. On the other hand, Latent Semantic Analysis (LSA) is one kind of approaches that is used to reveal the inherent correlation resided in co-occurrence activities, such as Web usage data. Moreover, LSA possesses the capability of capturing the hidden knowledge at semantic level that can’t be achieved by traditional methods. In this chapter, we aim to address building user communities of interests via combining Web usage mining and latent semantic analysis. Meanwhile we also present the application of user communities for Web recommendation.

Semantic Web Adaptation

2011 ◽  
pp. 78-88
Author(s):  
Alexander Mikroyannidis ◽  
Babis Theodoulidis
Keyword(s):  
Semantic Web ◽  
Web Sites ◽  
Web Usage Mining ◽  
New Era ◽  
Automatic Reasoning ◽  
Web Usage ◽  
User Friendly ◽  
Unstructured Information ◽  
The Web ◽  
The Way

The rate of growth in the amount of information available in the World Wide Web has not been followed by similar advances in the way this information is organized and exploited. Web adaptation seeks to address this issue by transforming the topology of a Web site to help users in their browsing tasks. In this sense, Web usage mining techniques have been employed for years to study how the Web is used in order to make Web sites more user-friendly. The Semantic Web is an ambitious initiative aiming to transform the Web to a well-organized source of information. In particular, apart from the unstructured information of today’s Web, the Semantic Web will contain machine-processable metadata organized in ontologies. This will enhance the way we search the Web and can even allow for automatic reasoning on Web data with the use of software agents. Semantic Web adaptation brings traditional Web adaptation techniques into the new era of the Semantic Web. The idea is to enable the Semantic Web to be constantly aligned to the users’ preferences. In order to achieve this, Web usage mining and text mining methodologies are employed for the semi-automatic construction and evolution of Web ontologies. This usage-driven evolution of Web ontologies, in parallel with Web topologies evolution, can bring the Semantic Web closer to the users’ expectations.

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.

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