![Loading...](https://link.springer.com/static/c4a417b97a76cc2980e3c25e2271af3129e08bbe/images/pdf-preview/spacer.gif)
-
Chapter
Towards a Methodology for Data Mining Project Development: The Importance of Abstraction
Standards such as CRISP-DM, SEMMA, PMML, are making data mining processes easier. Nevertheless, up to date, projects are being developed more as an art than as a science making it difficult to understand, eval...
-
Chapter and Conference Paper
Towards User Context Enhance Search Engine Logs Mining
Making search engines responsive to human needs requires understanding user intention when submitting a query. Intention, context and situation are intimately connected [8]. Thus context modelling is paramount...
-
Chapter
An Algorithm to Calculate the Expected Value of an Ongoing User Session
The fiercely competitive web-based electronic commerce environment has made necessary the application of intelligent methods to gather and analyze information collected from consumer web sessions. Knowledge ab...
-
Chapter and Conference Paper
Web Usage Mining Project for Improving Web-Based Learning Sites
Despite the great success of data mining being applied for personalization in web environments, it has not yet been massively applied in the e-learning domains. In this paper, we outline a web usage mining pro...
-
Chapter and Conference Paper
An Approach to Estimate the Value of User Sessions Using Multiple Viewpoints and Goals
Web-based commerce systems fail to achieve many of the features that enable small businesses to develop a friendly human relationship with customers. Although many enterprises have worried about user identific...
-
Chapter and Conference Paper
A Framework to Integrate Business Goals in Web Usage Mining
Web mining is a broad term that has been used to refer to the process of information discovery from Web sources: content, structure, and usage. Information collected by web servers and kept in the server log i...
-
Chapter and Conference Paper
Interval Estimation Naïve Bayes
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier called naïve Bayes is competitive with state of the art classifiers. This simple approach stands from assumptions of ...
-
Chapter and Conference Paper
Learning Semi Naïve Bayes Structures by Estimation of Distribution Algorithms
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier called naïve Bayes is competitive with state of the art classifiers. This simple approach stands from assumptions of ...