An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

This paper proposes an adaptive algorithm for clustering cumulative probability distribution functions (c.p.d.f.) of a continuous random variable, observed in different populations, into the minimum homogeneous clusters, making no parametric assumptions about the c.p.d.f.’s. The distance function for clustering c.p.d.f.’s that is proposed is based on the Kolmogorov–Smirnov two sample statistic. This test is able to detect differences in position, dispersion or shape of the c.p.d.f.’s. In our context, this statistic allows us to cluster the recorded data with a homogeneity criterion based on the whole distribution of each data set, and to decide whether it is necessary to add more clusters or not. In this sense, the proposed algorithm is adaptive as it automatically increases the number of clusters only as necessary; therefore, there is no need to fix in advance the number of clusters. The output of the algorithm are the common c.p.d.f. of all observed data in the cluster (the centroid) and, for each cluster, the Kolmogorov–Smirnov statistic between the centroid and the most distant c.p.d.f. The proposed algorithm has been used for a large data set of solar global irradiation spectra distributions. The results obtained enable to reduce all the information of more than 270,000 c.p.d.f.’s in only 6 different clusters that correspond to 6 different c.p.d.f.’s.

A novel information theory method for filter feature selection

In this paper, we propose a novel filter for feature selection. Such filter relies on the estimation of the mutual information between features and classes. We bypass the estimation of the probability density function with the aid of the entropic-graphs approximation of Rényi entropy, and the subsequent approximation of the Shannon one. The complexity of such bypassing process does not depend on the number of dimensions but on the number of patterns/samples, and thus the curse of dimensionality is circumvented. We show that it is then possible to outperform a greedy algorithm based on the maximal relevance and minimal redundancy criterion. We successfully test our method both in the contexts of image classification and microarray data classification.

Desarrollo cultural en las organizaciones: un modelo de estudio basado en la Grounded Theory

La cultura organizacional se configura a partir de la interrelación de los procesos de apropiación de la filosofía, la pertenencia, la adaptación, la satisfacción y el liderazgo compartidos por un grupo. Este conjunto de categorías puede ser reconocido mediante el uso de una matriz que incluye en su estructura subcategorías o conceptos y un conjunto de propiedades observables en el público interno. El presente artículo tiene por objetivo describir un modelo de estudio construido a partir de la Grounded Theory o Teoría Fundamentada que nos permita comprender el desarrollo cultural de las organizaciones. El estudio de caso se realizó en una compañía líder en Europa del sector de la distribución.

Diversity for Texts Builds in Language L(MT): Indexes Based in Theory of Information

If one has a distribution of words (SLUNs or CLUNS) in a text written in language L(MT), and is adjusted one of the mathematical expressions of distribution that exists in the mathematical literature, some parameter of the elected expression it can be considered as a measure of the diversity. But because the adjustment is not always perfect as usual measure; it is preferable to select an index that doesn't postulate a regularity of distribution expressible for a simple formula. The problem can be approachable statistically, without having special interest for the organization of the text. It can serve as index any monotonous function that has a minimum value when all their elements belong to the same class, that is to say, all the individuals belong to oneself symbol, and a maximum value when each element belongs to a different class, that is to say, each individual is of a different symbol. It should also gather certain conditions like they are: to be not very sensitive to the extension of the text and being invariant to certain number of operations of selection in the text. These operations can be theoretically random. The expressions that offer more advantages are those coming from the theory of the information of Shannon-Weaver. Based on them, the authors develop a theoretical study for indexes of diversity to be applied in texts built in modeling language L(MT), although anything impedes that they can be applied to texts written in natural languages.

Developing a programme theory to explain how primary health care teams learn to respond to intimate partner violence: a realist case-study

Background: Despite the progress made on policies and programmes to strengthen primary health care teams’ response to Intimate Partner Violence, the literature shows that encounters between women exposed to IPV and health-care providers are not always satisfactory, and a number of barriers that prevent individual health-care providers from responding to IPV have been identified. We carried out a realist case study, for which we developed and tested a programme theory that seeks to explain how, why and under which circumstances a primary health care team in Spain learned to respond to IPV. Methods: A realist case study design was chosen to allow for an in-depth exploration of the linkages between context, intervention, mechanisms and outcomes as they happen in their natural setting. The first author collected data at the primary health care center La Virgen (pseudonym) through the review of documents, observation and interviews with health systems’ managers, team members, women patients, and members of external services. The quality of the IPV case management was assessed with the PREMIS tool. Results: This study found that the health care team at La Virgen has managed 1) to engage a number of staff members in actively responding to IPV, 2) to establish good coordination, mutual support and continuous learning processes related to IPV, 3) to establish adequate internal referrals within La Virgen, and 4) to establish good coordination and referral systems with other services. Team and individual level factors have triggered the capacity and interest in creating spaces for team leaning, team work and therapeutic responses to IPV in La Virgen, although individual motivation strongly affected this mechanism. Regional interventions did not trigger individual and/ or team responses but legitimated the workings of motivated professionals. Conclusions: The primary health care team of La Virgen is involved in a continuous learning process, even as participation in the process varies between professionals. This process has been supported, but not caused, by a favourable policy for integration of a health care response to IPV. Specific contextual factors of La Virgen facilitated the uptake of the policy. To some extent, the performance of La Virgen has the potential to shape the IPV learning processes of other primary health care teams in Murcia.

A Quantile-Based Probabilistic Mean Value Theorem

For non-negative random variables with finite means we introduce an analogous of the equilibrium residual-lifetime distribution based on the quantile function. This allows us to construct new distributions with support (0, 1), and to obtain a new quantile-based version of the probabilistic generalization of Taylor's theorem. Similarly, for pairs of stochastically ordered random variables we come to a new quantile-based form of the probabilistic mean value theorem. The latter involves a distribution that generalizes the Lorenz curve. We investigate the special case of proportional quantile functions and apply the given results to various models based on classes of distributions and measures of risk theory. Motivated by some stochastic comparisons, we also introduce the “expected reversed proportional shortfall order”, and a new characterization of random lifetimes involving the reversed hazard rate function.

Model Probability in Self-organising Maps

Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced unsupervised self-organising network for the modelling of visual objects. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product.