Web Personalization using Neuro-Fuzzy Clustering Algorithms

Different users have different needs from the same web page and hence it is necessary to develop a system which understands the needs and demands of the users. Web server logs have abundant information about the nature of users accessing it. In this paper we discussed how to mine these web server logs for a given period of time using unsupervised and competitive learning algorithm like Kohonen\u27\u27s self organizing maps (SOM) and interpreting those results using Unified distance Matrix (U-matrix). These algorithms help us in efficiently clustering users based on similar web access patterns and each cluster having users with similar browsing patterns. These clusters are useful in web personalization so that it communicates better with its users and also in web traffic analysis for predicting web traffic at a given period of time

Knowledge Discovery in Virtual Worlds Usage Data: approaching Web Mining concepts to 3D Virtual Environments

[EN] This paper examines the relationships between Web and Virtual Worlds, and how these relationships can be used to approach concepts of knowledge discovery from Web Mining to 3D environments, such as Virtual Worlds. Also it will explain how to track information of usage data for knowledge discovery and what goals can be planned for this process. Every theoretical concept will be shown with examples, including the usage options to collect, data input to the entire process, relevant information extraction from raw data, techniques to discover knowledge and several considerations to decide and represent what knowledge is useful for the user. Based on these concepts a framework is presented in which, by comparison and approach to Web Usage Mining, may be defined an entire process of Knowledge Discovery and Data Analysis

Business Intelligence from Web Usage Mining

The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. In this paper, we present the important concepts of Web usage mining and its various practical applications. We further present a novel approach 'intelligent-miner' (i-Miner) to optimize the concurrent architecture of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy inference system to analyze the Web site visitor trends. A hybrid evolutionary fuzzy clustering algorithm is proposed in this paper to optimally segregate similar user interests. The clustered data is then used to analyze the trends using a Takagi-Sugeno fuzzy inference system learned using a combination of evolutionary algorithm and neural network learning. Proposed approach is compared with self-organizing maps (to discover patterns) and several function approximation techniques like neural networks, linear genetic programming and Takagi-Sugeno fuzzy inference system (to analyze the clusters). The results are graphically illustrated and the practical significance is discussed in detail. Empirical results clearly show that the proposed Web usage-mining framework is efficient

Using bi-clustering algorithm for analyzing online users activity in a virtual campus

Data mining algorithms have been proved to be useful for the processing of large data sets in order to extract relevant information and knowledge. Such algorithms are also important for analyzing data collected from the users' activity users. One family of such data analysis is that of mining of log files of online applications that register the actions of online users during long periods of time. A relevant objective in this case is to study the behavior of online users and feedback the design processes of online applications to provide better usability and adaption to users' preferences. The context of this work is that of a virtual campus in which thousands of students and tutors carry out the learning and teaching activity using online applications. The information stored in log files of virtual campuses tend to be large, complex and heterogeneous in nature. Hence, their mining requires both efficient and intelligent processing and analysis of user interaction data during long-term learning activities. In this paper, we present a bi-clustering algorithm for processing large log data sets from the online daily activity of students in a real virtual campus. Our approach is useful to extract relevant knowledge about user activity such as navigation patterns, activities performed as well as to study time parameters related to such activities. The extracted information can be useful not only to students and tutors to stimulate and improve their experience when interacting with the system but also to the designers and developers of the virtual campus in order to better support the online teaching and learning.Peer ReviewedPostprint (published version

Survey of data mining approaches to user modeling for adaptive hypermedia

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 applicatio

Modeling Web Navigation using Grammatical Inference

Abstract In this paper, a method that models user navigation on the Web, as opposed to a single Web site, is presented, aiming to assist the user by recommending pages. User modeling is done through data mining of Web usage logs, resulting in aggregate, rather than personal models. The proposed approach extends Grammatical Inference methods, by introducing an extra merging criterion, which examines the semantic similarity of automaton states. The experimental results showed that the method does indeed facilitate the modeling of Web navigation, which was not possible with the existing Web usage mining methods. However, a content-based recommendation model is shown to still outperform the proposed method, which suggests that the knowledge of the navigation sequence does not contribute to the recommendation process. This is due to the thematic cohesion of navigation sessions, in comparison to the large thematic diversity of Web usage data. Among three variants of the proposed method, the one based on Blue Fringe, that examines a larger space of possible merges, performs better