Metrics for the Adaptation of Site Structure

This paper presents an overview of metrics for web site structure and user navigation paths. Particular attention will be paid to the question what these metrics really say about a site and its usage, and how they can be applied for adapting navigation support to the mobile context

Revisitation Patterns and Disorientation

The non-linear structure of web sites may cause users to become disorientated. In this paper we describe the results of a pilot study to find measures of user revisitation patterns that help in predicting disorientation

Using web mining in e-commerce applications

Nowadays, the web is an important part of our daily life. The web is now the best medium of doing business. Large companies rethink their business strategy using the web to improve business. Business carried on the Web offers the opportunity to potential customers or partners where their products and specific business can be found. Business presence through a company web site has several advantages as it breaks the barrier of time and space compared with the existence of a physical office. To differentiate through the Internet economy, winning companies have realized that e-commerce transactions is more than just buying / selling, appropriate strategies are key to improve competitive power. One effective technique used for this purpose is data mining. Data mining is the process of extracting interesting knowledge from data. Web mining is the use of data mining techniques to extract information from web data. This article presents the three components of web mining: web usage mining, web structure mining and web content mining.e-commerce, web mining, web content mining, web structure mining, web usage mining

A Review – Clustering and Preprocessing For Web Log Mining

World Wide Web is consist of large amount of information and provides it different kinds users. Everyday number of users use log on internet.. Internet information growing enormously. Users accesses are documented in web logs. As huge storage log files are growing rapidly .One of the application of Data Mining is Web Usage Mining works on users logs. It consist of various steps such as user identification ,session identification and clustering. Again removing robot entries. In previous years data preprocessing analysis system algorithm on web usage mining has been used buts algorithm lacks on scalability problem. This proposes session identification process and building transaction preprocessing ,data cleaning by using efficient data mining algorithm . The experimental results may show considerable performance of proposed algorithm. DOI: 10.17762/ijritcc2321-8169.160415

Data Mining for Browsing Patterns in Weblog Data by Art Neural Networks

Categorising visitors based on their interaction with a website is a key problem in Web content usage. The clickstreams generated by various users often follow distinct patterns, the knowledge of which may help in providing customised content. This paper proposes an approach to clustering weblog data, based on ART2 neural networks. Due to the characteristics of the ART2 neural network model, the proposed approach can be used for unsupervised and self-learning data mining, which makes it adaptable to dynamically changing websites

Clustering of Web Users Using Session-based Similarity Measures

One important research topic in web usage mining is the clustering of web users based on their common properties. Informative knowledge obtained from web user clusters were used for many applications, such as the prefetching of pages between web clients and proxies. This paper presents an approach for measuring similarity of interests among web users from their past access behaviors. The similarity measures are based on the user sessions extracted from the user\u27s access logs. A multi-level scheme for clustering a large number of web users is proposed, as an extension to the method proposed in our previous work (2001). Experiments were conducted and the results obtained show that our clustering method is capable of clustering web users with similar interest

A review of data mining techniques for research in online shopping behaviour through frequent navigation paths

Knowing how consumers navigate online shopping web sites enables retailers to not only better design their sites for navigation but also place buying recommendations at strategic points and personalise the flow of content. Frequent navigation paths can be derived from browsing histories or clickstreams with sequence-oriented data mining techniques. In this working paper, we highlight, with examples, the relevance of frequent navigation paths to online shopping behaviour research and review some relevant data mining techniques