Abstract
Relatively simple transformations can speed up the execution of queries for data mining considerably. While some ILP systems use such transformations, relatively little is known about them or how they relate to each other. This paper describes a number of such transformations. Not all of them are novel, but there have been no studies comparing their efficacy. The main contributions of the paper are: (a) it clarifies the relationship between the transformations; (b) it contains an empirical study of what can be gained by applying the transformations; and (c) it provides some guidance on the kinds of problems that are likely to benefit from the transformations.

Abstract
The Watson distribution defined on the hypersphere is much used for modeling axial data. This distribution is rotationally symmetric about the modal axis. Then, in practice, before using the Watson distribution for modeling our data, it is better to test the hypothesis of rotational symmetry. For this purpose, we consider the test given by Prentice. In this paper, we determine the empirical power of this test, when data come from a Watson distribution vs. the alternative, where data come from a mixture of two Watson distributions.

Abstract
Introduction: Self-reported height and weight data have been used in several studies with the purpose of determining the prevalence of overweight and obesity. Despite being a simple methodology, little information exists about the reliability of these measures, namely, in university students. The objective of this study was to determine the sensitivity and specificity of self-reported body mass index (BMI) to evaluate the prevalence of overweight and obesity in university students. Methods: In a convenience sample of 380 university students (226 women and 154 men), weight and height were obtained by self-reported measures and anthropometrie assessment according to international standards methodology (objective). BMI was calculated from self-reported and direct measures. Results: The discrepancy between objective and self-reported weight was not significative. For height, this discrepancy was significantly different in women, in men, and between genders. The difference between BMI values was significantly different in women (0,8 ± 1,1 kg/m2), in men (0,4 ± 1,1 kg /m2) and between genders. Concerning overweight and obesity, according to the objective BMI, the sensitivity was only 50% in women, and 70% in men, while the specificity was 99% in women and 98% in men. Conclusion: Our results show a poor sensitivity of self-reported weight and height data, to estimate overweight and obesity, thus, this method might not be reliable for studies of prevalence of obesity in this population.

Abstract
In this paper we provide a different way to estimate matrices B and D, in subspace identification algorithms. The starting point was the method proposed by Van Overschee and De Moor [10] - the only one applying subspace ideas to the estimation of those matrices. We have derived new (and simpler) expressions and we found that the method proposed by Van Overschee and De Moor [10] can be rewritten as a weighted least squares problem, involving the future outputs and inputs.

Abstract
In this paper, two approaches, for the estimation of matrices A and C in CV A-type subspace identification algorithms are compared and the differences between the two obtained estimates are analysed. One of the methods, "least squares" based, was proposed in the original CVA algorithm. The other method, inspired in the techniques of the classical Realization Theory and proposed by Verhaegen, is far more efficient. Therefore, although the two methods produce two different estimates, a replacement is proposed and an expression for correction of the estimates is obtained, in order to reduce the loss of accuracy.

Abstract
In this paper, a recursive algorithm is presented, based on the CVA subspace identification algorithm. The main idea was to explore the relations between the orthogonal and oblique projections involved and to provide simpler expressions that allowed a recursive version of the algorithm - guaranteeing most of the advantages of this kind of methods, and still improving the numerical efficiency.