NOESIS: A Framework for Complex Network Data Analysis

Network data mining has attracted a lot of attention since a large number of real-world problems have to deal with complex network data. In this paper, we present NOESIS, an open-source framework for network-based data mining. NOESIS features a large number of techniques and methods for the analysis of structural network properties, network visualization, community detection, link scoring, and link prediction. ­e proposed framework has been designed following solid design principles and exploits parallel computing using structured parallel programming. NOESIS also provides a stand-alone graphical user interface allowing the use of advanced software analysis techniques to users without prior programming experience. ­is framework is available under a BSD open-source software license.The NOESIS project was partially supported by the Spanish Ministry of Economy and the European Regional Development Fund (FEDER), under grant TIN2012–36951, and the Spanish Ministry of Education under the program “Ayudas para contratos predoctorales para la formación de doctores 2013” (predoctoral grant BES–2013–064699)

An Overview of Alternative Rule Evaluation Criteria and Their Use in Separate-and-Conquer Classifiers

Separate-and-conquer classifiers strongly depend on the criteria used to choose which rules will be included in the classification model. When association rules are employed to build such classifiers (as in ART [3]), rule evaluation can be performed attending to different criteria (other than the traditional confidence measure used in association rule mining). In this paper, we analyze the desirable properties of such alternative criteria and their effect in building rule-based classifiers using a separate-and-conquer strategy

Dependencias funcionales difusas en bases de datos relacionales difusas

La memoria tiene como finalidad definir un modelo relacional de bases de datos difusas y una teoría de diseño concebida desde una perspectiva difusa. Para ello se han resuelto los siguientes puntos: -se han analizado las propiedades básicas que cualquier definición de dependencia funcional difusa ha de satisfacer. -hemos introducido el concepto de dependencia funcional difusa basada en reglas, como pilar del modelo de diseño que proponemos. -hemos abordado el problema de la redundancia de una base de datos relacional difusa, y su eliminación a través de operadores de proyección. -podemos aislar la información relativa a una dependencia difusa en una relación independiente, de forma que se recupere la información original a través de la reunión difusa. -la consecución de los anteriores puntos nos ha permitido establecer una teoría de normalización de una base de datos relacional difusa. la memoria termina presentando las líneas de investigación que proponemos seguir. se incluye también un ejemplo de aplicación para ilustrar el proceso de diseño de una base de datos relacional difusa, así como la bibliografía utilizadaTesis de Granada. Departamento de Ciencias de la Computación e Inteligencia Artificia

An Automorphic Distance Metric and Its Application to Node Embedding for Role Mining

This work was partially supported by the Spanish Ministry of Economy and the European Regional Development Fund (FEDER), under grant TIN2012-36951, and the program "Ayudas para contratos predoctorales para la formacion de doc 2013," under grant BES-2013-064699. This work was also partially supported by the project "BIGDATAMED: Analisis de datos en Medicina, de las historias clinicas al BIGDATA" with references B-TIC-145-UGR18 and P18RT-1765.Role is a fundamental concept in the analysis of the behavior and function of interacting entities in complex networks. Role discovery is the task of uncovering the hidden roles of nodes within a network. Node roles are commonly defined in terms of equivalence classes. Two nodes have the same role if they fall within the same equivalence class. Automorphic equivalence, where two nodes are equivalent when they can swap their labels to form an isomorphic graph, captures this notion of role. )e binary concept of equivalence is too restrictive, and nodes in real-world networks rarely belong to the same equivalence class. Instead, a relaxed definition in terms of similarity or distance is commonly used to compute the degree to which two nodes are equivalent. In this paper, we propose a novel distance metric called automorphic distance, which measures how far two nodes are from being automorphically equivalent. We also study its application to node embedding, showing how our metric can be used to generate role-preserving vector representations of nodes. Our experiments confirm that the proposed automorphic distance metric outperforms a state-of-the-art automorphic equivalence-based metric and different state-of-the-art techniques for the generation of node embeddings in different role-related tasks.Spanish Government TIN2012-36951European Commission TIN2012-36951program "Ayudas para contratos predoctorales para la formacion de doc 2013" BES-2013-064699project "BIGDATAMED: Analisis de datos en Medicina, de las historias clinicas al BIGDATA" B-TIC-145-UGR18 P18RT-176

Client/server architecture for fuzzy relational databases

This paper shows a FRDBMS architecture whose main characteristics are: 1) it is implemented entirely on classical RDBMS just using the resources provided by them, 2) it preserves all the operations and qualities of the host RDBMS and gives them more power adding new capabilities to deal with "fuzzy" information and 3) it provides a frame to develop applications which exploit fuzzy information

Client/server architecture for fuzzy relational databases

This paper shows a FRDBMS architecture whose main characteristics are: 1) it is implemented entirely on classical RDBMS just using the resources provided by them, 2) it preserves all the operations and qualities of the host RDBMS and gives them more power adding new capabilities to deal with "fuzzy" information and 3) it provides a frame to develop applications which exploit fuzzy information

Client/server architecture for fuzzy relational databases

This paper shows a FRDBMS architecture whose main characteristics are: 1) it is implemented entirely on classical RDBMS just using the resources provided by them, 2) it preserves all the operations and qualities of the host RDBMS and gives them more power adding new capabilities to deal with fuzzy information and 3) it provides a frame to develop applications which exploit fuzzy information