The use of web structure and content to identify subjectively interesting web usage patterns

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.

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.

Extracting knowledge from Web user's access data in Web Usage Mining (WUM) process is challenging task that is continuing to gain importance as the size of the web and its user-base increase. That's why meaningful methods have been proposed in the literature in order to understand the behaviour of the user in the web and improve the access modes to information. In this present work, we propose to emerge the community detection technique in WUM process, so we propose an approach of data extraction based on the modularity function. The obtained results illustrate the aptitude of the proposed algorithm to determine the optimal solution and to improve the Web design.

Web usage mining is the application of data mining techniques to usage logs of large Web data repositories in order to produce results used in some aspects, such as Web site design, Web server design, users classification, creating adaptive Web sites and Web site personalization. Data preprocessing is a critical step in Web usage mining. The results of data preprocessing are relevant to the next steps, such as transaction identification, path analysis, association rules mining, sequential patterns mining, and so forth. An algorithm called "USIA" was presented and its advantage and disadvantage were analyzed, USIA is experimentally evaluated that not only its efficiency is high, but also it can identify user and session exactly.

Recent surveys have revealed that about 199 million of active and over 1.2 Billion of inactive websites exist across the globe. The categories of websites have also increased beyond espionage networks of spies, computer networks for corporate organizations, networks for governments' agencies, networks for social interactions, search engines and networks for religious bodies, etc. These diversities have generated complex issues regarding the morphology and classification of webs and web mining. Thus, the validity of the generic web classification, web mining taxonomy, and contemporary studies on the regularities of web usage, web content, web semantic, web structures, and the process of extracting useful information and interesting patterns from the intricate of the Internet are frequently questionable. The existing web mining taxonomy can also lead to misinformation, misclassification, and crisscrossed issues such that numerous webs' patterns could be marked with crossing and inexplicable lines. By using qualitative virtual interviews of 26 skilled web-designers and a focus group-conference of 7 experts in web-usage to brainstorm on the above issues, this chapter comprehensively discusses the above concepts and how they relate to web classification and web mining taxonomy. The themes obtained elucidate the techniques that commonly underpin basic web mining taxonomy. New concepts like existence of esoteric web data, exoteric web data; mysterious, inexplicable, and mystifying patterns; and cryptic vocabularies are discussed to assist web analytics. Finally, the author suggests eight classification attributes for web mining patterns (illustrative, expositive, educative, advisory, interpretative, demonstrative, revealing, and informatory) and proposes a new web mining taxonomy to minimize the impacts of the above concerns on global settings.

The prolific growth of web-based applications and the enormous amount of data involved therein led to the development of techniques for identifying patterns in the web data. Web mining refers to the application of data mining techniques to the World Wide Web. Web usage mining is the process of extracting useful information from web server logs based on the browsing and access patterns of the users. The information is especially valuable for business sites in order to achieve improved customer satisfaction. Based on the user’s needs, Web Usage Mining discovers interesting usage patterns from web data in order to understand and better serve the needs of the web based application. Web Usage Mining is used to discover hidden patterns from weblogs. It consists of three phases like Preprocessing, pattern discovery and Pattern analysis. In this paper, we present each phase in detail, the process of extracting useful information from server log files and some of application areas of Web Usage Mining such as Education, Health, Human-computer interaction, and Social media.

Web Usage Mining is the application of data mining techniques to large Web log databases in order to extract usage patterns. A cross-transaction association rule describes the association relationships among different user transactions in Web logs. In this paper, a Linear time intra-transaction frequent itemsets mining algorithm and the closure property of frequent itemsets are used to mining cross-transaction association rules from web log databases. We give the related preliminaries and present an efficient algorithm for efficient mining frequent cross-transaction closed pageviews sets in large Web log database. An extensive performance study shows that our algorithm can mining cross-transaction web usage patterns from large database efficiently.

This chapter aims to master web mining and Information Retrieval (IR) in the digital age, thus describing the overviews of web mining and web usage mining; the significance of web mining in the digital age; the overview of IR; the concept of Collaborative Information Retrieval (CIR); the evaluation of IR systems; and the significance of IR in the digital age. Web mining can contribute to the increase in profits by selling more products and by minimizing costs. Web mining is the application of data mining techniques to discover the interesting patterns from web data in order to better serve the needs of web-based multifaceted applications. Mining web data can improve the personalization, create the selling opportunities, and lead to more profitable relationships with customers in global business. Web mining techniques can be applied with the effective analysis of the clearly understood business needs and requirements. Web mining builds the detailed customer profiles based on the transactional data. Web mining is used to create the personalized search engines which can recognize the individuals' search queries by analyzing and profiling the web user's search behavior. IR is the process of obtaining relevant information from a collection of informational resources. IR has considerably changed with the expansion of the Internet and the advent of modern and inexpensive graphical user interfaces and mass storage devices. The effective IR system, including an active indexing system, not only decreases the chances that information will be misfiled but also expedites the retrieval of information. Regarding IR utilization, the resulting time-saving benefit increases office efficiency and productivity while decreasing stress and anxiety. Most IR systems provide the advanced searching capabilities that allow users to create the sophisticated queries. The chapter argues that applying web mining and IR has the potential to enhance organizational performance and reach strategic goals in the digital age.

Internet is the biggest source of information which is easily accessible but unstructured. Web mining - is the application of data mining techniques to discover patterns from the Web. According to analysis targets, web mining can be divided into three different types, which are Web usage mining, Web content mining and Web structure mining. [3] Web usage mining reveals about user's navigation pattern at any website through analyzing web logs. In this study we have tried to analyze the users online job seeking tendency pattern through two available web-based tools viz. Google Trends and Google Zeitgeist.

Information seekers rely heavily on search engines to extract relevant information because of the Internet's exponential development in users and traffic. The availability of a vast amount of textual, audio, video, and other content has expanded search engines' duty. Users of the Internet can obtain pertinent information about their query from the search engine by using factors like content and link structure. It does not, however, imply that the information is accurate. The link structure of web sites is analyzed using Web Structure Mining (WSM), and their content is analyzed using Web Content Mining (WCM), which determines how well the ranking module performs.A multitude of content-based recommender systems are currently in use, and they are well-researched in both text acquisition and filtering. These systems recommend documents based on text analysis. The management information system can benefit from Web Content Ming technology. Web content mining is the process of extracting or mining knowledge or useful information from web pages. The purpose of this work is to investigate web content extraction technology enhanced Reinforcement algorithm which anticipates user interest by analyzing the page according to user view related topic.This ERIM procedure involves locating web sites linked to user queries and using hyperlinks to locate a collection of related web pages and find the topic categorization using machine learning method.

Web Usage Mining (WUM) includes all the Data Mining techniques used to analyze the behavior of a Web site‘s users (Cooley, Mobasher & Srivastava, 1999, Spiliopoulou, Faulstich & Winkler, 1999, Mobasher, Dai, Luo & Nakagawa, 2002). Based mainly on the data stored into the access log files, these methods allow the discovery of frequent behaviors. In particular, the extraction of sequential patterns (Agrawal, & Srikant, 1995) is well suited to the context of Web logs analysis, given the chronological nature of their records. On a Web portal, one could discover for example that “25% of the users navigated on the site in a particular order, by consulting first the homepage then the page with an article about the bird flu, then the Dow Jones index evolution to finally return on the homepage before consulting their personal e-mail as a subscriber”. In theory, this analysis allows us to find frequent behaviors rather easily. However, reality shows that the diversity of the Web pages and behaviors makes this approach delicate. Indeed, it is often necessary to set minimum thresholds of frequency (i.e. minimum support) of about 1% or 2% before revealing these behaviors. Such low supports combined with significant characteristics of access log files (e.g. huge number of records) are generally the cause of failures or limitations for the existent techniques employed in Web usage analysis. A solution for this problem consists in clustering the pages by topic, in the form of a taxonomy for example, in order to obtain a more general behavior. Considering again the previous example, one could have obtained: “70% of the users navigate on the Web site in a particular order, while consulting the home page then a page of news, then a page on financial indexes, then return on the homepage before consulting a service of communication offered by the Web portal”. A page on the financial indexes can relate to the Dow Jones as well as the FTSE 100 or the NIKKEI (and in a similar way: the e-mail or the chat are services of communication, the bird flu belongs to the news section, etc.). Moreover, the fact of grouping these pages under the “financial indexes” term has a direct impact by increasing the support of such behaviors and thus their readability, their relevance and significance. The drawback of using a taxonomy comes from the time and energy necessary to its definition and maintenance. In this chapter, we propose solutions to facilitate (or guide as much as possible) the automatic creation of this taxonomy allowing a WUM process to return more effective and relevant results. These solutions include a prior clustering of the pages depending on the way they are reached by the users. We will show the relevance of our approach in terms of efficiency and effectiveness when extracting the results.

Web usage mining is the application of data mining techniques to discover usage patterns and behaviors from web data (clickstream, purchase information, customer information, etc.) in order to unde...

Web sites are one of the most important tools for advertisements in international area for universities and other foundation. The quality of a website can be evaluated by analyzing user accesses of the website. To know the quality of a web site user accesses are to be evaluated by web usage mining. The results of mining can be used to improve the website design and increase satisfaction which helps in various applications. Log files are the best source to know user behavior. But the raw log files contains unnecessary details like image access, failed entries etc., which will affect the accuracy of pattern discovery and analysis. So preprocessing stage is an important work in mining to make efficient pattern analysis. To get accurate mining results user's session details are to be known.In our proposed work, user's browsing behavior on a web page is considered with different modes of actions on client side to calculate web page browsing time with more precision and accuracy. It helps us to maintain the accuracy in finding frequent web usage patterns and administrators to evaluate its website usage more effectively. In our proposed work we considered t, t1, t2 three time limits which are subjective to the website management. These values should be selected effectively because if we select small value it will increase load on client side and if we select large value it will not provide good results.

Web mining is the application of data mining technique to automatically discover and gathered information from web documents and services which can be in structured, unstructured or semi-structured form. It is used to understand user behaviour, evaluate the effectiveness of a particular web and find out the relevant and efficient results from the web. Accuracy and relevance of information extracting from the web is the most significant issue of concern for the realization of web mining. The idea is to improve the accuracy and relevance of information extracting from the web. This paper proposes a novel model for improving the web mining, we hypothesize that web mining is Semantic-Synaptic web mining. Semantic-Synaptic web Mining interlinks the web of data to different data sources at low entropy (Information Theory). This paper combines the best ideas from the semantic web and synaptic web at low entropy and constructs the architecture of Semantic-Synaptic web mining.

During users visiting a Web Server Access, data is stored. This information can be used broadly. Using this information we can obtain users' preferences and use them to personalize Web pages. Web mining is a new discussion that has been proposed to manage the web pages. In fact Web mining is the application of data mining techniques to discover patterns of user interests is the Data Web. In this article we provide a structure for web mining.