Fernando Crespo

Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives

Clustering is one of the most widely used approaches in data mining with real life applications in virtually any domain. The huge interest in clustering has led to a possibly three-digit number of algorithms with the k-means family probably the most widely used group of methods. Besides classic bivalent approaches, clustering algorithms belonging to the domain of soft computing have been proposed and successfully applied in the past four decades. Bezdeks fuzzy c-means is a prominent example for such soft computing cluster algorithms with many effective real life applications. More recently, Lingras and West enriched this area by introducing rough k-means. In this article we compare k-means to fuzzy c-means and rough k-means as important representatives of soft clustering. On the basis of this comparison, we then survey important extensions and derivatives of these algorithms; our particular interest here is on hybrid clustering, merging fuzzy and rough concepts. We also give some examples where k-means, rough k-means, and fuzzy c-means have been used in studies.

Can Nutraceuticals Prevent Alzheimer's Disease? Potential Therapeutic Role of a Formulation Containing Shilajit and Complex B Vitamins

Alzheimers disease (AD) is a brain disorder displaying a prevalence and impact in constant expansion. This expansive and epidemic behavior is concerning medical and public opinion while focusing efforts on its prevention and treatment. One important strategy to prevent this brain impairment is based on dietary changes and nutritional supplements, functional foods and nutraceuticals. In this review we discuss the potential contributions of shilajit and complex B vitamins to AD prevention. We analyze the status of biological studies and present data of a clinical trial developed in patients with mild AD. Studies suggest that shilajit and its active principle fulvic acid, as well as a formula of shilajit with B complex vitamins, emerge as novel nutraceutical with potential uses against this brain disorder.

Uncertainty modeling in dynamic clustering — A soft computing perspective

Uncertainty plays an important role in clustering. For example in customer segmentation we may be faced with the situation that a certain customer not necessarily belongs to just one segment, i.e. his/her class assignment is uncertain. Several cluster algorithms have been proposed that employ uncertainty modeling in different ways. The most frequently used techniques are probability theory, fuzzy logic, and recently rough sets. If uncertainty modeling is already important in static clustering this becomes even more important in dynamic clustering where several elements of the respective cluster can change over time. Changes produce uncertainty and that is where uncertainty modeling in dynamic clustering comes into play. In this paper we present briefly two cluster algorithms that employ soft computing approaches and provide a comparison regarding their capabilities to capture uncertainties in dynamic environments. Future research issues for this area are also identified.

Rough Clustering Approaches for Dynamic Environments

Economies are characterized by constant change. This change has several facets ranging from long term effects like economic cycles and short term financial distortion caused by rumors. It also includes socio-economic technological trends or seasonal alteration and many others. “The only constant is change”, the famous saying often credited to the Greek philosopher Heraclitus, summarizes the challenging environment organizations are confronted with. Hence, for any organization, like companies, government agencies or also small family enterprises, one of the main challenges is to discover economic and technological changes as early as possible to smoothly adapt to upcoming new trends or seasonal oscillation. To successfully deal with changing environments dynamic approaches to data mining have gained increasing importance in the last decades. Areas of application range from engineering and management to science and others. In this chapter we introduce to dynamic rough k-means clustering and discuss a real life application in the retail sector.

Predicting sentencing outcomes with centrality measures

Despite their importance for stakeholders in the criminal justice system, few methods have been developed for determining which criminal behavior variables will produce accurate sentence predictions. Some approaches found in the literature resort to techniques based on indirect variables, but not on the social network behavior with exception of the work of Baker and Faulkner [ASR 58: 837–860, 1993]. Using information on the Caviar Network narcotics trafficking group as a real-world case, we attempt to explain sentencing outcomes employing the social network indicators. Specifically, we report the ability of centrality measures to predict a) the verdict (innocent or guilty) and b) the sentence length in years. We show that while the set of indicators described by Baker and Faulkner yields good predictions, introduction of the additional centrality measures generates better predictions. Some ideas for orienting future research on further improvements to sentencing outcome prediction are discussed.

An Illustrative Comparison of Rough k-Means to Classical Clustering Approaches

Rough clustering has gained increasing attention in the last decade with applications in such diverse areas like bioinformatics, traffic control and retail. The relationship between rough clustering and, in particular, fuzzy and possibilistic concepts is still a topic that is raised first and foremost by practitioners who are looking for an adequate clustering algorithm. Therefore, we compare rough k-means to fuzzy c-means, possibilistic c-means and to classical k-means in our paper. We show that rough k-means is closer related to classical k-means than to fuzzy and possibilistic c-means. Besides brief theoretical evaluations we perform illustrative experiments on artificial data and the IRIS data.

Categorización social y cogniciones infantiles sobre la pobreza en niños: una mirada desde el esencialismo psicológico

El termino “desierto alimentario” son a aquellas areas que se caracterizan por la escasez o ausencia de comercios de alimentacion. Los primeros estudios localizaron tales areas en los barrios socialmente desfavorecidos de las ciudades, cuyo limitado acceso a los supermercados condicionaria la dieta y la salud de sus residentes. Sin embargo, estas afirmaciones no han estado exentas de polemica. El objetivo de este trabajo es realizar una aproximacion teorica al concepto de “desierto alimentario”. Para ello, en primer lugar, se definen que son y cuales han sido las principales aportaciones a su estudio. En segundo lugar, se exponen las criticas y el debate que han surgido en los ultimos anos en diferentes paises. Las conclusiones muestran la complejidad del estudio del acceso a la alimentacion debido a los multiples factores que intervienen en la oferta y el consumo alimentario.Resumen Despues de que en 1995 la Union Europea comenzara las negociaciones para un acuerdo de asociacion con el mercosur , finalmente, ha sido el proceso de integracion centroamericano el primero en consensuar un acuerdo historico con el bloque europeo, que se constituye en el primer pacto inter-regional de la historia, que engloba tres pilares: el dialogo politico, el comercial y de inversiones y la cooperacion para el desarrollo. Se trata de un acuerdo que no deja indiferente a nadie, no solo por sus diferencias con el rd - cafta , sino por la apertura a nuevos mercados que satisfacen las necesidades de unos, mientras empeoran los problemas de otros. Sin duda, servira de modelo para los convenios sucesivos entre la Union Europea y el mercosur , la Comunidad, la asean o el ccgResumen El articulo presenta una reflexion acerca de las decisiones tomadas en relacion con la politica y la economia de Irak desde el momento de la invasion de Estados Unidos y el Reino Unido. Mas alla de centrarse en la discusion sobre la legitimidad de la invasion, se presenta un debate sobre las consecuencias que las contradicciones en las decisiones tomadas y la ausencia de una estrategia economica, social y politica han ocasionado en el pais. Se ponen de manifiesto los actores, intereses, relaciones de poder y decisiones tomadas en diferentes momentos del llamado proceso de transicion que se pone en cuestion. Abstract The article deals with a reflection about the decisions taken in relation to Iraq politics and economy from the moment of USA and United Kingdom invasion. Beyond focusing on the discussion about the legitimacy of the invasion, a debate is presented about the consequences originated by the contradictions in the decisions taken and the absence of a political, social and economic strategy. Actors, concerns, power relationships and decisions taken in different moments of the so called transition process that is questioned, Guerra, sociedad civil, politica internacional.come into scene.RESUMEN Los principales aciertos y desaciertos del programa de becas ALBAN son considerados, en este articulo, como representativos de la dinamica de cooperacion entre la Union Europea y America Latina. Sobre esta base, se analizan de forma sintetica, sus resultados mas importantes. Siendo asi, el presente articulo pretende contribuir con el diseno de instrumentos mas eficaces de cooperacion que dinamicen las relaciones entre estas dos regiones durante el periodo 2014-2020.Social protest is characterized by coordinated efforts and new organization red performance that emphasized cultural codes and communitarian roles. Theses mobilization and organization forms call attention of mass media, people in general and stimulating academic thinking with different approaches as well as new social movements (NSM). Different forms social organization has been presence in last decade in Tandil city, Argentina. Protest related with environmental effect mine’s activity, or actions against polluted activities, and so on. Particularly, we characterized water’s fight process a local level since historiography and documental information. This allowed us detecting conceptual analogy with similar process globally called new social movements.

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases

Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers–Logistic Regression, Naïve Bayes and Random Forest–with a range of social network measures and the necessary databases to model the verdicts in two real–world cases: the U.S. Watergate Conspiracy of the 1970’s and the now–defunct Canada–based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures.

Exploring the prominence of Romeo and Juliet’s characters using weighted centrality measures

Why are Romeo and Juliet prominent characters in Shakespeare’s play of the same name? Contrary to what common sense might suggest, the academic literature does not provide a unique answer to this question. Indeed, there is little agreement on who the main character is and which elements of a script contribute to establishing a character’s leading role. The objective of this article is to explore and compare the prominence of characters in Romeo and Juliet by using social network analysis. To this end, we calculate the centralities of several characters in Romeo and Juliet using a method based on Social Network Analysis. Comparing the scores generated by this analysis, we found that Romeo’s centrality is more stable than Juliet’s while hers is lower and supported by the ‘strength of the bonds’ she develops with other characters. Thus, the comparison of different centrality rankings and clusters provides new knowledge about the plays of Shakespeare. We show that the ‘strength’ of the relationships affects the prominence of the characters. This finding opens new directions for analyzing Shakespeare’s scripts and determining who the main character is using weighted centrality measures. Finally, we discuss some theoretical and practical implications of the method used in this study.