Rough Sets Clustering and Markov model for Web Access Prediction

Discovering user access patterns from web access log is increasing the importance of information to build up adaptive web server according to the individual user’s behavior. The variety of user behaviors on accessing information also grows, which has a great impact on the network utilization. In this paper, we present a rough set clustering to cluster web transactions from web access logs and using Markov model for next access prediction. Using this approach, users can effectively mine web log records to discover and predict access patterns. We perform experiments using real web trace logs collected from www.dusit.ac.th servers. In order to improve its prediction ration, the model includes a rough sets scheme in which search similarity measure to compute the similarity between two sequences using upper approximation

Pillar 3 and Modelling of Stakeholders’ Behaviour at the Commercial Bank Website during the Recent Financial Crisis

AbstractThe paper analyses domestic and foreign market participants’ interests in mandatory Basel 2, Pillar 3 information disclosure of a commercial bank during the recent financial crisis. The authors try to ascertain whether the purposes of Basel 2 regulations under the Pillar 3 - Market discipline, publishing the financial and risk related information, have been fulfilled. Therefore, the paper focuses on modelling of visitors’ behaviour at the commercial bank website where information according to Basel 2 is available. The authors present a detailed analysis of the user log data stored by web servers. The analysis can help better understand the rate of use of the mandatory and optional Pillar 3 information disclosure web pages at the commercial bank website in the recent financial crisis in Slovakia. The authors used association rule analysis to identify the association among content categories of the website. The results show that there is in general a small interest of stakeholders in mandating the commercial bank's disclosure of financial information. Foreign website visitors were more concerned about information disclosure according to Pillar 3, Basel 2 regulation, and they have less interest in general information about the bank than domestic ones

Optimal algorithms for finding user access sessions from very large Web logs

Although efficient identification of user access sessions from very large web logs is an unavoidable data preparation task for the success of higher level web log mining, little attention has been paid to algorithmic study of this problem. In this paper we consider two types of user access sessions, interval sessions and gap sessions. We design two efficient algorithms for finding respectively those two types of sessions with the help of new data structures. We present both theoretical and empirical analysis of the algorithms and prove that both algorithms have optimal time complexity

Improving the Performance of a Proxy Server using Web log mining

Web caching techniques have been widely used with the objective of caching as many web pages and web objects in the proxy server cache as possible to improve network performance. Web pre-fetching schemes have also been widely discussed where web pages and web objects are pre-fetched into the proxy server cache. This paper presents an approach that integrates web caching and web pre-fetching approach to improve the performance of proxy server’s cache

Integrating Network Analysis and Data Mining Techniques into Effective Framework for Web Mining and Recommendation. A Framework for Web Mining and Recommendation

The main motivation for the study described in this dissertation is to benefit from the development in technology and the huge amount of available data which can be easily captured, stored and maintained electronically. We concentrate on Web usage (i.e., log) mining and Web structure mining. Analysing Web log data will reveal valuable feedback reflecting how effective the current structure of a web site is and to help the owner of a web site in understanding the behaviour of the web site visitors. We developed a framework that integrates statistical analysis, frequent pattern mining, clustering, classification and network construction and analysis. We concentrated on the statistical data related to the visitors and how they surf and pass through the various pages of a given web site to land at some target pages. Further, the frequent pattern mining technique was used to study the relationship between the various pages constituting a given web site. Clustering is used to study the similarity of users and pages. Classification suggests a target class for a given new entity by comparing the characteristics of the new entity to those of the known classes. Network construction and analysis is also employed to identify and investigate the links between the various pages constituting a Web site by constructing a network based on the frequency of access to the Web pages such that pages get linked in the network if they are identified in the result of the frequent pattern mining process as frequently accessed together. The knowledge discovered by analysing a web site and its related data should be considered valuable for online shoppers and commercial web site owners. Benefitting from the outcome of the study, a recommendation system was developed to suggest pages to visitors based on their profiles as compared to similar profiles of other visitors. The conducted experiments using popular datasets demonstrate the applicability and effectiveness of the proposed framework for Web mining and recommendation. As a by product of the proposed method, we demonstrate how it is effective in another domain for feature reduction by concentrating on gene expression data analysis as an application with some interesting results reported in Chapter 5