Abstract
Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we propose an adaptation of association rules for label ranking. The adaptation, which is illustrated in this work with APRIORI Algorithm, essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. We also adapt the method to make a prediction from the possibly conflicting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, the results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.

Abstract
This paper describes the study on example selection in regression problems using mu-SVM (Support Vector Machine) linear as prediction algorithm. The motivation case is a study done on real data for a problem of bus trip time prediction. In this study we use three different training sets: all the examples, examples from past days similar to the day where prediction is needed, and examples selected by a CART regression tree. Then, we verify if the CART based example selection approach is appropriate on different regression data sets. The experimental results obtained are promising.

Abstract
Traditionally, recommender systems for the Web deal with applications that have two types of entities/dimensions, users and items. With these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a direct method that enriches the information in the access logs with new dimensions. We empirically test this method with two recommender systems, an item-based collaborative filtering technique and association rules, on three data sets. Our results show that while collaborative filtering is not able to take advantage of the new dimensions added, association rules are capable of profiting from our direct method. © 2008 IEEE.

Abstract
Clusterings based on many variables are difficult to visualize and interpret. We present a methodology based on Factor Analysis (FA) which can be used for that purpose. FA generates a small set of variables which encode most of the information in the original variables. We apply the methodology to segment the users of a web portal, using access log data. It not only makes it simpler to visualize and understand the clusters which are obtained on the original variables but it also helps the analyst in selecting some of the original variables for further analysis of those clusters.

Abstract
We present a methodology for the personalization of e-newsletters based on the analysis of user access logs. To approach the problem we have used clustering on the set of users, described by their web access patterns. Our work is evaluated using a case study with real data from e-newsletters sent by mail to users of a web portal, and can be adapted to similar situations. Positive results were obtained, indicating that the methodology is able to automatically select contents for a personalized e-newsletter.

Abstract
We present a web-based system to monitor the quality of the meta-data used to describe content in web portals. The system implements meta-data analysis using statistical, visualization and data mining tools. The web-based system enables the site's editor to detect and correct problems in the description of contents, thus improving the quality of the web portal and the satisfaction of its users. We have developed this system and tested it on a Portuguese portal for management executives.