Website Structure Improvement Based on the Combination of Selected Web Structure and Web Usage Mining Methods

2018 ◽  
Vol 17 (06) ◽  
pp. 1743-1776 ◽  
Author(s):  
Jozef Kapusta ◽  
Michal Munk ◽  
Martin Drlik
Keyword(s):  
Web Mining ◽  
Web Usage Mining ◽  
Point Of View ◽  
Web Pages ◽  
Web Page ◽  
Web Usage ◽  
Web Structure ◽  
Web Structure Mining ◽  
Mining Methods ◽  
The Web

The different web mining methods and techniques can help to solve some typical issues of the contemporary websites, contribute to more effective personalization, improve a website structure and reorganize its web pages. However, only several papers tried to combine web structure and web usage mining (WUM) methods with this aim. The paper researches if and how the combination of selected web structure and WUM methods can identify misplaced web pages and how they can contribute to improving the website structure. The paper analyzes the relationship between the estimated importance of the web page from the web page creator’s point of view using the web structure mining method based on PageRank and visitors’ real perception of the importance of that individual web page using the WUM method based on sequence patterns analysis, which eliminates the problem with repeated visits of the same web page during one session. The results prove that the expected probability of accesses to the individual web page correlates with the observed visit rate obtained from the log files using the WUM method. Furthermore, the website can be improved based on the consequent application of the residual analysis on the obtained results. The applicability of the proposed combination of the web structure and WUM methods is presented on two case studies from different application domains of the contemporary web. As a result, the web pages, which are underestimated or overestimated by the web page creators, are successfully identified in both cases.

Performance Comparison of Data Mining Classifiers on Web Log Data

2020 ◽  
Vol 17 (11) ◽  
pp. 5113-5116
Author(s):  
Varun Malik ◽  
Vikas Rattan ◽  
Jaiteg Singh ◽  
Ruchi Mittal ◽  
Urvashi Tandon
Keyword(s):  
Web Mining ◽  
Performance Comparison ◽  
Web Usage Mining ◽  
Classification Algorithms ◽  
Web Content ◽  
Web Usage ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
The Web

Web usage mining is the branch of web mining that deals with mining of data over the web. Web mining can be categorized as web content mining, web structure mining, web usage mining. In this paper, we have summarized the web usage mining results executed over the user tool WMOT (web mining optimized tool) based on the WEKA tool that has been used to apply various classification algorithms such as Naïve Bayes, KNN, SVM and tree based algorithms. Authors summarized the results of classification algorithms on WMOT tool and compared the results on the basis of classified instances and identify the algorithms that gives better instances accuracy.

Web Usage Mining through Associative Models

2011 ◽  
pp. 1231-1234 ◽  
Author(s):  
Paolo Giudici ◽  
Paola Cerchiello
Keyword(s):  
Web Site ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Page ◽  
User Session ◽  
Web Usage ◽  
Associative Models ◽  
The Web

The aim of this contribution is to show how the information, concerning the order in which the pages of a Web site are visited, can be profitably used to predict the visit behaviour at the site. Usually every click corresponds to the visualization of a Web page. Thus, a Web clickstream defines the sequence of the Web pages requested by a user. Such a sequence identifies a user session.

Web Mining for Public E-Services Personalization

2010 ◽  
pp. 751-758
Author(s):  
P. Markellou
Keyword(s):  
Web Site ◽  
Web Mining ◽  
Web Usage Mining ◽  
Web Content ◽  
Web Personalization ◽  
Business Transactions ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
The Web

Over the last decade, we have witnessed an explosive growth in the information available on the Web. Today, Web browsers provide easy access to myriad sources of text and multimedia data. Search engines index more than a billion pages and finding the desired information is not an easy task. This profusion of resources has prompted the need for developing automatic mining techniques on Web, thereby giving rise to the term “Web mining” (Pal, Talwar, & Mitra, 2002). Web mining is the application of data mining techniques on the Web for discovering useful patterns and can be divided into three basic categories: Web content mining, Web structure mining, and Web usage mining. Web content mining includes techniques for assisting users in locating Web documents (i.e., pages) that meet certain criteria, while Web structure mining relates to discovering information based on the Web site structure data (the data depicting the Web site map). Web usage mining focuses on analyzing Web access logs and other sources of information regarding user interactions within the Web site in order to capture, understand and model their behavioral patterns and profiles and thereby improve their experience with the Web site. As citizens requirements and needs change continuously, traditional information searching, and fulfillment of various tasks result to the loss of valuable time spent in identifying the responsible actor (public authority) and waiting in queues. At the same time, the percentage of users who acquaint with the Internet has been remarkably increased (Internet World Stats, 2005). These two facts motivate many governmental organizations to proceed with the provision of e-services via their Web sites. The ease and speed with which business transactions can be carried out over the Web has been a key driving force in the rapid growth and popularity of e-government, e-commerce, and e-business applications. In this framework, the Web is emerging as the appropriate environment for business transactions and user-organization interactions. However, since it is a large collection of semi-structured and structured information sources, Web users often suffer from information overload. Personalization is considered as a popular solution in order to alleviate this problem and to customize the Web environment to users (Eirinaki & Vazirgiannis, 2003). Web personalization can be described, as any action that makes the Web experience of a user personalized to his or her needs and wishes. Principal elements of Web personalization include modeling of Web objects (pages) and subjects (users), categorization of objects and subjects, matching between and across objects and/or subjects, and determination of the set of actions to be recommended for personalization. In the remainder of this article, we present the way an e-government application can deploy Web mining techniques in order to support intelligent and personalized interactions with citizens. Specifically, we describe the tasks that typically comprise this process, illustrate the future trends, and discuss the open issues in the field.

Web Mining for Public E-Services Personalization

2009 ◽  
pp. 1079-1086 ◽  
Author(s):  
Penelope Markellou ◽  
Angeliki Panayiotaki ◽  
Athanasios Tsakalidis
Keyword(s):  
Web Site ◽  
Web Mining ◽  
Web Usage Mining ◽  
Web Content ◽  
Web Personalization ◽  
Business Transactions ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
The Web

Over the last decade, we have witnessed an explosive growth in the information available on the Web. Today, Web browsers provide easy access to myriad sources of text and multimedia data. Search engines index more than a billion pages and finding the desired information is not an easy task. This profusion of resources has prompted the need for developing automatic mining techniques on Web, thereby giving rise to the term “Web mining” (Pal, Talwar, & Mitra, 2002). Web mining is the application of data mining techniques on the Web for discovering useful patterns and can be divided into three basic categories: Web content mining, Web structure mining, and Web usage mining. Web content mining includes techniques for assisting users in locating Web documents (i.e., pages) that meet certain criteria, while Web structure mining relates to discovering information based on the Web site structure data (the data depicting the Web site map). Web usage mining focuses on analyzing Web access logs and other sources of information regarding user interactions within the Web site in order to capture, understand and model their behavioral patterns and profiles and thereby improve their experience with the Web site. As citizens requirements and needs change continuously, traditional information searching, and fulfillment of various tasks result to the loss of valuable time spent in identifying the responsible actor (public authority) and waiting in queues. At the same time, the percentage of users who acquaint with the Internet has been remarkably increased (Internet World Stats, 2005). These two facts motivate many governmental organizations to proceed with the provision of e-services via their Web sites. The ease and speed with which business transactions can be carried out over the Web has been a key driving force in the rapid growth and popularity of e-government, e-commerce, and e-business applications. In this framework, the Web is emerging as the appropriate environment for business transactions and user-organization interactions. However, since it is a large collection of semi-structured and structured information sources, Web users often suffer from information overload. Personalization is considered as a popular solution in order to alleviate this problem and to customize the Web environment to users (Eirinaki & Vazirgiannis, 2003). Web personalization can be described, as any action that makes the Web experience of a user personalized to his or her needs and wishes. Principal elements of Web personalization include modeling of Web objects (pages) and subjects (users), categorization of objects and subjects, matching between and across objects and/or subjects, and determination of the set of actions to be recommended for personalization. In the remainder of this article, we present the way an e-government application can deploy Web mining techniques in order to support intelligent and personalized interactions with citizens. Specifically, we describe the tasks that typically comprise this process, illustrate the future trends, and discuss the open issues in the field.

Web Mining for Public E-Services Personalization

2011 ◽  
pp. 1629-1634
Author(s):  
P. Markellou
Keyword(s):  
Web Site ◽  
Web Mining ◽  
Web Usage Mining ◽  
Web Content ◽  
Web Personalization ◽  
Business Transactions ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
The Web

Over the last decade, we have witnessed an explosive growth in the information available on the Web. Today, Web browsers provide easy access to myriad sources of text and multimedia data. Search engines index more than a billion pages and finding the desired information is not an easy task. This profusion of resources has prompted the need for developing automatic mining techniques on Web, thereby giving rise to the term “Web mining” (Pal, Talwar, & Mitra, 2002). Web mining is the application of data mining techniques on the Web for discovering useful patterns and can be divided into three basic categories: Web content mining, Web structure mining, and Web usage mining. Web content mining includes techniques for assisting users in locating Web documents (i.e., pages) that meet certain criteria, while Web structure mining relates to discovering information based on the Web site structure data (the data depicting the Web site map). Web usage mining focuses on analyzing Web access logs and other sources of information regarding user interactions within the Web site in order to capture, understand and model their behavioral patterns and profiles and thereby improve their experience with the Web site. As citizens requirements and needs change continuously, traditional information searching, and fulfillment of various tasks result to the loss of valuable time spent in identifying the responsible actor (public authority) and waiting in queues. At the same time, the percentage of users who acquaint with the Internet has been remarkably increased (Internet World Stats, 2005). These two facts motivate many governmental organizations to proceed with the provision of e-services via their Web sites. The ease and speed with which business transactions can be carried out over the Web has been a key driving force in the rapid growth and popularity of e-government, e-commerce, and e-business applications. In this framework, the Web is emerging as the appropriate environment for business transactions and user-organization interactions. However, since it is a large collection of semi-structured and structured information sources, Web users often suffer from information overload. Personalization is considered as a popular solution in order to alleviate this problem and to customize the Web environment to users (Eirinaki & Vazirgiannis, 2003). Web personalization can be described, as any action that makes the Web experience of a user personalized to his or her needs and wishes. Principal elements of Web personalization include modeling of Web objects (pages) and subjects (users), categorization of objects and subjects, matching between and across objects and/or subjects, and determination of the set of actions to be recommended for personalization. In the remainder of this article, we present the way an e-government application can deploy Web mining techniques in order to support intelligent and personalized interactions with citizens. Specifically, we describe the tasks that typically comprise this process, illustrate the future trends, and discuss the open issues in the field.

A Heuristic Mining Algorithm Using Web Hyperlink Structure

2010 ◽  
Vol 108-111 ◽  
pp. 11-16
Author(s):  
Chun Lai Chai
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Web Usage Mining ◽  
Web Content ◽  
Web Content Mining ◽  
Web Structure ◽  
Mining Algorithm ◽  
Web Structure Mining ◽  
Content Mining ◽  
The Web

Web mining aims to discover useful information or knowledge from the Web hyperlink structure, page content and usage log. Based on the primary kind of data used in the mining process, Web mining tasks are categorized into three main types: Web structure mining, Web content mining and Web usage mining. Following is what they do on Web Data Mining. This paper proposed a heuristic mining algorithm.

Web Fuzzy Clustering Processing Model

2011 ◽  
Vol 219-220 ◽  
pp. 98-102
Author(s):  
Kai Xi Xie ◽  
Ting Gui Chen
Keyword(s):  
Fuzzy Clustering ◽  
Web Mining ◽  
Web Usage Mining ◽  
Web Page ◽  
Clustering Method ◽  
Web Usage ◽  
User Clustering ◽  
Web Page Clustering ◽  
Web User Clustering ◽  
The Web

In this paper, we combine the web mining and fuzzy clustering and give the concept of web fuzzy clustering processing model and its application. We also introduce the web fuzzy direct clustering method in brief. Web fuzzy clustering can be used in the web user clustering and web page clustering of web usage mining.

Research on Ranking Algorithms in Web Structure Mining

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Suvarna Sharma ◽  
Amit Bhagat
Keyword(s):  
Web Mining ◽  
Review Paper ◽  
Search Algorithm ◽  
Web Pages ◽  
Page Rank ◽  
The Past ◽  
Ranking Algorithms ◽  
Web Structure ◽  
Web Structure Mining ◽  
The Web

In the past few decades, the Web has emerged as a treasure of information and web mining is a technique to handle this treasure. During recent years web mining has been a well-researched area. Web mining is the application of the data mining which is useful to extract the knowledge from web. With the progress of web, more and more data are now available for users on web. Web structure mining deals with the contents and hyperlinks on web pages. In this review paper, we have focused on three basic algorithms for evaluating the importance of pages i.e. Page Rank, Weighted Page Rank, and Hyperlink-Induced Topic Search and comparison of those algorithms. Page Rank algorithm is based on back links of the page and it calculates the rank of web pages at indexing time. Weighted Page Rank algorithm scores pages according to their relevancies and rank of a page is calculated by its number of incoming and outgoing links. Hyperlink-induced topic search algorithm is an iterative algorithm developed to quantify each pages value as an authority and as a hub. This study was done basically to explore the link structure algorithms for ranking pages.

First Look on Web Mining Techniques to Improve Business Intelligence of E-Commerce Applications

2017 ◽  
pp. 298-314 ◽  
Author(s):  
G. Sreedhar ◽  
A. Anandaraja Chari
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Web Content ◽  
Web Data ◽  
Web Data Mining ◽  
Useful Knowledge ◽  
Web Usage ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining

Web Data Mining is the application of data mining techniques to extract useful knowledge from web data like contents of web, hyperlinks of documents and web usage logs. There is also a strong requirement of techniques to help in business decision in e-commerce. Web Data Mining can be broadly divided into three categories: Web content mining, Web structure mining and Web usage mining. Web content data are content availed to users to satisfy their required information. Web structure data represents linkage and relationship of web pages to others. Web usage data involves log data collected by web server and application server which is the main source of data. The growth of WWW and technologies has made business functions to be executed fast and easier. As large amount of transactions are performed through e-commerce sites and the huge amount of data is stored, valuable knowledge can be obtained by applying the Web Mining techniques.

Effectiveness of Web Usage Mining Techniques in Business Application

2017 ◽  
pp. 324-350 ◽  
Author(s):  
Ahmed El Azab ◽  
Mahmood A. Mahmood ◽  
Abd El-Aziz
Keyword(s):  
Data Mining ◽  
Academic Research ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Data ◽  
Web Data Mining ◽  
Web Usage ◽  
Business Application ◽  
Common Interests ◽  
The Web

Web usage mining techniques and applications across industries is still exploratory and, despite an increase in academic research, there are challenge of analyze web which quantitatively capture web users' common interests and characterize their underlying tasks. This chapter addresses the problem of how to support web usage mining techniques and applications across industries by combining language of web pages and algorithms that used in web data mining. Existing research in web usage mining techniques tend to focus on finding out how each techniques can apply in different industries fields. However, there is little evidence that researchers have approached the issue of web usage mining across industries. Consequently, the aim of this chapter is to provide an overview of how the web usage mining techniques and applications across industries can be supported.

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