An Overview on Web Usage Mining

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.

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.

The discipline of Web Usage Mining has grown rapidly in the past few years, despite the crash of the e-commerce boom of the late 1990s. Web Usage Mining is the application of data mining techniques to Web clickstream data in order to extract usage patterns. Yet, with all of the resources put into the problem, claims of success have been limited and are often tied to specific Web site properties that are not found in general. One reason for the limited success has been a component of Web Usage Mining that is often overlooked---the need to understand the content and structure of a Web site. The processing and quantification of a Web sites content and structure for all but completely static and single frame Web sites is arguably one of the most difficult tasks to automate in the Web Usage Mining process. This article shows that, not only is the Web Usage Mining process enhanced by content and structure, it cannot be completed without it. The results of experiments run on data from a large e-commerce site are presented to show that proper preprocessing cannot be completed without the use of Web site content and structure, and that the effectiveness of pattern analysis is greatly enhanced.

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 mining is one of the mining technologies, which applies data mining techniques in large amount of web log data. Web navigational mining discovers users’ access patterns from web logs. This information can be used to identify the behavior of the web user. However, the web data will grow rapidly in the short time, and some of the web data may be antiquated. The user behavior may be changed when the new web data is inserted into and the old web data is deleted from web logs. Therefore, the user behavior must be re-discovered from the updated web logs. However, it is very time-consuming to re-find the users’ access patterns. Hence, many researchers pay attention to the incremental mining, which utilizes the previous mining results and finds new patterns just from the inserted or deleted part of the web logs such that the mining time can be reduced. The present paper proposes an efficient incremental web navigational mining algorithm for discovering web navigational patterns when the user sequences are inserted into and deleted from original database. It avoids re-finding the original web navigational patterns and re-counting the original candidate sequences. It uses lattice structure to keep the previous mining results such that just new candidate sequences need to be computed. Hence, the web navigational patterns can be obtained rapidly when the navigational sequence database is updated. Besides, maximal web navigational patterns can also be obtained easily by traversing the lattice structure. The experimental results show that the present algorithm is more efficient than the other approaches.

This paper builds on the original data mining and modelling research which has proposed the discovery of novel structural relation patterns, applying the approach in web usage mining. The focus of attention here is on concurrent access patterns CAP, where an overarching framework illuminates the methodology for web access patterns post-processing. Data pre-processing, pattern discovery and patterns analysis all proceed in association with access patterns mining, CAP mining and CAP modelling. Pruning and selection of access patterns takes place as necessary, allowing further CAP mining and modelling to be pursued in the search for the most interesting concurrent access patterns. It is shown that higher level CAPs can be modelled in a way which brings greater structure to bear on the process of knowledge discovery. Experiments with real-world datasets highlight the applicability of the approach in web navigation.

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.

Web mining is one of the mining technologies, which applies data mining techniques in large amount of Web data to improve the Web services. Web traversal pattern mining discovers most of the users' access patterns from Web logs. This information can provide the navigation suggestions for Web users such that appropriate actions can be adopted. However, the Web data will grow rapidly in the short time, and some of the Web data may be antiquated. The user behaviors may be changed when the new Web data is inserted into and the old Web data is deleted from Web logs. Therefore, we must re-discover the user behaviors from the updated Web logs. However, it is very time-consuming to re-find the users' access patterns. Hence, many researchers pay attention to the incremental mining in recent years. The essence of incremental mining is that it utilizes the previous mining results and finds new patterns just from the inserted or deleted part of the Web logs such that the mining time can be reduced. In this paper, we propose an efficient incremental Web traversal pattern mining algorithm. The experimental results show that our algorithm is more efficient than the other approaches

This chapter discusses Web usage mining techniques that can be applied for building adaptive hypermedia systems. These techniques are used for uncovering hidden patterns within Web access data and then for building the user model that lies in the heart of each adaptive system. Web access data, traditionally stored in the server log files, constitute a rich source of data collected in a non-intrusive way that guards the privacy of users. Several Web usage mining approaches have been proposed for exposing usage patterns, with the most prominent ones being cluster mining, association rule mining, and sequential pattern mining. This chapter provides an overview of the state of the art in research of Web usage mining, and discusses the most relevant criteria for deciding on the suitability of these techniques for building an adaptive Web site. Moreover, the different types of patterns revealed from Web usage mining are correlated with different adaptation aspects.

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.

The purpose of this paper is to provide a more current evaluation and update of web mining research and techniques available. Current advances in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining. For each tabulated research work, we examine such key issues as web mining process, methods/techniques, applications, data sources, and software used. Unlike previous investigators, we divide web mining processes into the following five subtasks: (1) resource finding and retrieving, (2) information selection and preprocessing, (3) patterns analysis and recognition, (4) validation and interpretation, and (5) visualization. This paper also reports the comparisons and summaries of selected software for web mining. The web mining software selected for discussion and comparison in this paper are SPSS Clementine, Megaputer PolyAnalyst, ClickTracks by web analytics, and QL2 by QL2 Software Inc. Applications of these selected web mining software to available data sets are discussed together with abundant presentations of screen shots, as well as conclusions and future directions of the research.

The web usage mining uses data mining techniques to discover interesting usage patterns from web data. Web personalization uses web usage mining techniques for the process of customization. Customization involves knowledge acquisition done by analysis of user's navigational behavior. A user when goes online would like to get the links which suits his requirements or usage in the website he visits. The next business requirement in the online industry will be personalizing/customizing the web page fulfilling for each individuals requirement. The personalization of the web page will involve clustering of different web pages having common usage pattern. As the size of the cluster goes on increasing due to increase in users or growth of interest of users it will become inevitable need to optimize the clusters. This paper proposes a cluster optimizing methodology based on ants nestmate recognition ability and is used for eliminating the data redundancies that may occur after the clustering done by the web usage mining methods. For clustering an ART1-neural network based approach is used. "AntNestmate approach for cluster optimization" is presented to personalize web page clusters of target users.

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.

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.