Abstract
Feature selection is one of the most important concepts in data mining when dimensionality reduction is needed. The performance measures of feature selection encompass predictive accuracy and result comprehensibility. Consistency based feature selection is a significant category of feature selection research that substantially improves the comprehensibility of the result using the parsimony principle. In this work, the feature selection algorithm LAID, Logical Analysis of Inconsistent Data, is applied to large volumes of data. In order to deal with hundreds of thousands of attributes, a problem de-composition strategy associated with a set covering problem formulation is used. The algorithm is applied to artificial datasets with genome-like characteristics of patients with rare diseases.

Abstract
Nowadays an important issue, for an organization is to be able to implement relevant anti-bribery risk management systems with mandatory laws. Managers strive to reach an equilibrium between a pure mandatory rule oriented organization and people freedom of choice to mitigate bribery on organization. The problem is how to develop and manage efficiently anti-bribery system in an organization without putting at risk its day by day operation. They are concerned how to balance between deep control and flexible way of people work on organization. The purpose of this document is to introduce a decision-making way of defining a context to establish an anti-bribery risk management system in accordance with the best practices. To address this matter, we will support our work in a theoretical framework for the analysis of human work and introduce anti-bribery as non-functional requirement (generic qualities of services) of organization information systems.

Abstract
In social network analysis the identification of communities and the discovery of brokers is a very important issue. Community detection typically uses partition techniques. In this work the information extracted from social networking goes beyond cohesive groups, enabling the discovery of brokers that interact between communities. The partition is found using a set covering formulation, which allows the identification of actors that link two or more dense groups. Our algorithm returns the needed information to create a good visualization of large networks, using a condensed graph with the identification of the brokers.

Abstract
We present a sensibility analysis and new visualizations using an improved version of the Ramex-Forum algorithm applied to the study of the petroleum production chain. Different combinations of parameters and new ways to visualize data will be used. Results will highlight the importance of Ramex-Forum and its proper parameterizations for analyzing relevant relations among price variations in petroleum and other similar markets.

Abstract
The sports facilities that offer regular sports services have been adopting ERP and CRM systems and there are now databases with historical data of great value. In this work, we demonstrate that by applying predictive models to these data it is possible to identify abandonment profiles. Based on the profiles found, experience planning is carried out, with test and control groups, in order to find concrete actions of loyalty.