The world-wide web (WWW) is the largest distributed information space and has grown to encompass diverse information resources. Although the web is growing exponentially, the individual’s capacity to read and digest content is essentially fixed. The full economic potential of the web will not be realized unless enabling technologies are provided to facilitate access to web resources. Currently web personalization is the most promising approach to remedy this problem, and web mining, particularly web-usage mining, is considered a crucial component of any efficacious web-personalization system. In this paper, we describe a complete framework for web-usage mining to satisfy the challenging requirements of web-personalization applications. For on-line and anonymous web personalization to be effective, web usage mining must be accomplished in real time as accurately as possible. On the other hand, web-usage mining should allow a compromise between scalability and accuracy to be applicable to real-life websites with numerous visitors. Within our web-usage-mining framework, we introduce a distributed user-tracking approach for accurate, scalable, and implicit collection of the usage data. We also propose a new model, the feature-matrices (FM) model, to discover and interpret users’ access patterns. With FM, various spatial

During data mining process users and algorithms could take advantage of the experiences other users or algorithms (agents) had. Implicit Culture...

The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the application. Index Terms—Adaptive hypermedia (AH), data mining, machine learning, user modeling (UM). I.

In this paper, we present a novel approach to group users according to their Web access patterns. Our technique for grouping users is based on the ART1 neural network. We compare the quality of clustering of our ART1 based clustering technique with that of the K-Means clustering algorithm in terms of inter-cluster and intra-cluster distances. Our results show that the average inter-cluster distance of the clusters formed by K-Means algorithm varies from 12.66 to 24.20, while the average inter-cluster distance of clusters formed by our ART1 based clustering technique is almost constant (approximately 18.01), which indicates the high quality of clusters formed by our approach. We present a prefetching scheme in which we apply our clustering technique to group users and then prefetch their requests according to the prototype vector of each group. Our prefetching scheme has prediction accuracy as high as 97.78%.