Abstract
This paper proposes a classification approach to identify the team's formation (formation means the strategical layout of the players in the field) in the robotic soccer domain for the two dimensional (213) simulation league. It is a tool for decision support that allows the coach to understand the strategy of the opponent. To reach that goal we employ Data Mining classification techniques. TO understand the simulated robotic soccer domain we briefly describe the Simulation system, some related work and the use of Data Mining techniques for the detection of formations. In order to perform a robotic soccer match with different formations we develop a way to configure the formations in a training base team (FC Portugal) and a data preparation process. The paper describes the base team and the test team,, used and the respective configuration process. After the matches between test teams the data is subjected to a reduction process taking into account the players' position in the field given the collective. In the modeling stage appropriate learning algorithms were selected. In the solution analysis, the error rate (% incorrectly classify instances) with the statistic test t-Student for paired samples were selected, as the evaluation measure. Experimental results show that it is possible to automatically identify the formations used by the base team (FC Portugal) in distinct matches against different opponents, using Data Mining techniques. The experimental results also show that the SMO (Sequential Minimal Optimization) learning algorithm has the best performance.

Abstract
Travel time prediction is an important tool for the planning tasks of mass transit and logistics companies. ID this paper we investigate the use of regression methods for the problem of predicting the travel time of buses in a Portuguese public transportation company. More specifically, we empirically evaluate the impact of varying parameters on the performance of different regression algorithms, such as support vector machines (SVM), random forests (RF) and projection pursuit, regression (PPR). We also evaluate the impact of the focusing tusks (example selection; domain value definition and feature selection) in the accuracy of those algorithms. Concerning the algorithms, we observe that 1) RF is quite robust to the choice of parameters and focusing methods: 2) the choice of parameters for SVM can be made independently of focusing methods while 3) for PPR they should be selected simultaneously. For the focusing methods, we observe that a stronger effect is obtained using example selection, particularly in combination with SVM.

Abstract
This work presents a method of knowledge discovery in data obtained from Molecular Dynamics Protein Unfolding Simulations. The data under study was obtained from simulations of the unfolding process of the protein Transthyretin (TTR), responsible for amyloid diseases such as Familial Amyloid Polyneuropathy (FAP). Protein unfolding and misfolding are at the source of many amyloidogenic diseases. Thus, the molecular characterization of protein unfolding processes through experimental and simulation methods may be essential in the development of effective treatments. Here, we analyzed the distance variation of each of the 127 amino acids C. (alpha carbon) atoms of TTR to the centre of mass of the protein, along 10 different unfolding simulations - five simulations of WT-TTR and five simulations of L55P-TTR, a highly amyloidogenic TTR variant. Using data mining techniques, and considering all the information of the 10 runs, we identified several clusters of amino acids. For each cluster we selected the representative element and identified events which were used as features. With Association Rules we found patterns that characterize the type of TTR variant under study. These results may help discriminate between amyloidogenic and non-amyloidogenic behaviour among different TTR variants and contribute to the understanding of the molecular mechanisms of FAP.

Abstract
The potential value of a market segmentation for a company is usually assessed in terms of six criteria: identifiability, substantiality, accessibility, responsiveness, stability and actionability. These are widely accepted as essential criteria, but they are difficult to quantify. Quantification is particularly important in early stages of the segmentation process, especially when automatic clustering methods are employed. With such methods it is easy to produce a large number of segmentations but only the most interesting ones should be selected for further analysis. In this paper, we address the problem of how to quantify the value of a segmentation according to the criteria above. We propose several measures and test them on a case study, consisting of a segmentation of portal users.

Abstract
Visualization in data mining is typically related to data exploration. In this chapter we present a methodology for the post processing and visualization of association rule models. One aim is to provide the user with a tool that enables the exploration of a large set of association rules. The method is inspired by the hypertext metaphor. The initial set of rules is dynamically divided into small comprehensible sets or pages, according to the interest of the user. From each set, the user can move to other sets by choosing one appropriate operator. The set of available operators transform sets of rules into sets of rules, allowing focusing on interesting regions of the rule space. Each set of rules can also be then seen with different graphical representations. The tool is web-based and dynamically generates SVG pages to represent graphics. Association rules are given in PMML format. © 2008 Springer-Verlag Berlin Heidelberg.

Abstract
In this paper we propose a methodology for automatically retrieving document collections from the web on specific topics and for organizing them and keeping them up-to-date over time, according to user specific persistent information needs. The documents collected are organized according to user specifications and are classified partly by the user and partly automatically. A presentation layer enables the exploration of large sets of documents and, simultaneously, monitors and records user interaction with these document collections. The quality of the system is permanently monitored; the system periodically measures and stores the values of its quality parameters. Using this quality log it is possible to maintain the quality of the resources by triggering procedures aimed at correcting or preventing quality degradation.