Extending FuzAtAnalyzer to approach the management of classical negation

FuzAtAnalyzer was conceived as a Java framework which goes beyond of classical tools in formal concept analysis. Specifically, it successfully incorporated the management of uncertainty by means of methods and tools from the area of fuzzy formal concept analysis. One limitation of formal concept analysis is that they only consider the presence of properties in the objects (positive attributes) as much in fuzzy as in crisp case. In this paper, a first step in the incorporation of negations is presented. Our aim is the treatment of the absence of properties (negative attributes). Specifically, we extend the framework by including specific tools for mining knowledge combining crisp positive and negative attributes.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The direct-optimal basis via reductions

Formal Concept Analysis has become a real approach in the trend Information-Knowledge-Wisdom. It turns around the mining of a data set to built a concept lattice which provides an strong structure of the knowledge. Implications play the role of an alternative specification of this concept lattice and may be managed by means of inference rules. This syntactic treatment is guided by several properties like directness, minimality, optimality, etc. In this work, we propose a method to calcu- late the direct-optimal basis equivalent to a given Implicational System. Our method deals with unitary and non-unitary implications. Moreover, it shows a better performance that previous methods in the literature by means of the use of Simplification Logic and reduction paradigm, which remains narrow implications in any stage of the process. We have also developed an empirical study to compare our method with previous approaches in the literature.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A logic-based approach to compute a direct basis from implications

Formal Concept Analysis is an emergent area in the topic of data analysis based on lattice theory. In this framework, a context is defined as the relation between a set of objects and a set of attributes and from here it is possible to extract relevant knowledge. One of the important topics is to study the implications between the attributes considered. In a context, some equivalent sets of implications can be compute using different techniques. We are studying the direct optimal basis, which enables us to compute the closure of a set of attributes in just one iteration. A Prolog method has been implemented that computes a direct basis from a set of implications.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech, TIN12-39353-C04-01 and TIN2011-2808

Program transformations using temporal logic side conditions

This paper describes an approach to program optimisation based on transformations, where temporal logic is used to specify side conditions, and strategies are created which expand the repertoire of transformations and provide a suitable level of abstraction. We demonstrate the power of this approach by developing a set of optimisations using our transformation language and showing how the transformations can be converted into a form which makes it easier to apply them, while maintaining trust in the resulting optimising steps. The approach is illustrated through a transformational case study where we apply several optimisations to a small program

Synthesis of Attributed Feature Models From Product Descriptions: Foundations

Feature modeling is a widely used formalism to characterize a set of products (also called configurations). As a manual elaboration is a long and arduous task, numerous techniques have been proposed to reverse engineer feature models from various kinds of artefacts. But none of them synthesize feature attributes (or constraints over attributes) despite the practical relevance of attributes for documenting the different values across a range of products. In this report, we develop an algorithm for synthesizing attributed feature models given a set of product descriptions. We present sound, complete, and parametrizable techniques for computing all possible hierarchies, feature groups, placements of feature attributes, domain values, and constraints. We perform a complexity analysis w.r.t. number of features, attributes, configurations, and domain size. We also evaluate the scalability of our synthesis procedure using randomized configuration matrices. This report is a first step that aims to describe the foundations for synthesizing attributed feature models