Browsing by Author "Santos, Jorge M. A."
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- An Algorithm to Discover the k-Clique Cover in Networks.Publication . Cavique, Luís; Mendes, Armando B.; Santos, Jorge M. A.In social network analysis, a k-clique is a relaxed clique, i.e., a kclique is a quasi-complete sub-graph. A k-clique in a graph is a sub-graph where the distance between any two vertices is no greater than k. The visualization of a small number of vertices can be easily performed in a graph. However, when the number of vertices and edges increases the visualization becomes incomprehensible. In this paper, we propose a new graph mining approach based on k-cliques. The concept of relaxed clique is extended to the whole graph, to achieve a general view, by covering the network with k-cliques. The sequence of k-clique covers is presented, combining small world concepts with community structure components. Computational results and examples are presented.
- Um algoritmo para encontrar a cobertura por k-cliques em redes sociaisPublication . Mendes, Armando B.; Cavique, Luís; Santos, Jorge M. A.Na análise de redes sociais, uma k-clique é a relaxação de uma clique, i.e., uma k-clique é um quase sub-grafo completo. Um k-clique num grafo é um sub-grafo onde a distancia entre quaisquer par de vértices não é maior que k. A visualização de um pequeno número de vértices é fácil de obter. Contudo, quando o número de vértices aumenta a visualização torna-se incompreensível. Nesta comunicação, propomos uma nova abordagem na extracção de conhecimento em grafos, utilizando k-cliques. O conceito que clique relaxado é estendido para todo o grafo, de forma a ter uma visão geral, ao cobrir a rede com k-cliques. Sequências de coberturas de k-cliques são apresentadas combinando o conceito dos "pequenos mundos" com estruturas com coesão. Resultados computacionais e exemplos são apresentados.
- Efficiency Assessment: Final RemarksPublication . Santos, Jorge M. A.; Silva, Emiliana; Mendes, Armando B.In this concluding chapter the editors make some important remarks on the importance and conclusions of the book as a whole, focusing on its importance, relevance and adequacy to an extended audience of researchers in the efficiency analysis in the agricultural and environment fields.
- Efficiency Measures in the Agricultural Sector: The BeginningPublication . Silva, Emiliana; Mendes, Armando B.; Santos, Jorge M. A.The agricultural productivity is often based on non-parametric models 5 (DEA), or stochastic models (SFA). In this initial article, the editors start by 6 pointing that the models (DEA and SFA) allow estimating the efficiency of the 7 production frontier and their structural forms. Then, it is presented, in general terms, 8 the differences between DEA and SFA models: DEA model involves the use of 9 technical linear programming to construct a non-parametric piecewise surface, and 10 SFA models comprise econometricmodels with a random variable, or an error term, 11 including two components: one to account for random effects and another to take 12 care of the technical inefficiency effects. Finally, it shows a comparison between 13 the two approaches (SFA and DEA) and the advantages and disadvantages of their 14 utilizations.
- Mathematical programming applied to benchmarking in economics and managementPublication . Santos, Jorge M. A.; Mendes, Armando B.; Cavique, Luís; Kapelko, MagdalenaIn the recent years, as a result of the economic crisis, there is a pressing need for new management tools and statistical methods to compare firms seeking better results. Comparison of firms with best observed performance is getting an increasing in importance due to the large amount of data which can be extracted from the Web. In this work a review of quantitative benchmarking techniques based on Data Envelopment Analysis is presented with examples derived from the widely available datasets.
- Multiplier adjustment in data envelopment analysisPublication . Santos, Jorge M. A.; Cavique, Luís; Mendes, Armando B.Weights restriction is a well-known technique in the DEA field. When those techniques are applied, weights cluster around its new limits, making its evaluation dependent of its levels. This paper introduces a new approach to weights adjustment by Goal Programming techniques, avoiding the imposition of hard restrictions that can even lead to unfeasibility. This method results in models that are more flexible.
- Open-source software in operations research in engineering teachingPublication . Caseiro, Palmira; Santos, Jorge M. A.; Cavique, Luís; Mendes, Armando B.This contribution will focus on Computational Tools of Open-Source Software that are rather interesting in teaching Operations Research applied to engineering sciences, (Tavares et al 1996) specifically some educational experiences in the area of Forecasting; Simulation; Graphs and Networks; Decision Theory and Linear Programming. We introduce some examples with RStudio v0.97 (Verzani, 2008) especially in the forecasting area and with Scilab 5.4.1 (Barreto, 2011) for Graphs and Networks (Shortest Path and maximum Flow) and the remaining topics (Taha, 2007) with an Open Source Spreadsheet: Gnumeric 1.12.0.
- Open-Source software in OR educationPublication . Santos, Jorge M. A.; Cavique, Luís; Mendes, Armando B.This contribution will focus on Computational Tools of Open-Source Software in OR Education. Some educational experiences in the area of Forecasting; Simulation; Graphs and Networks; Decision Theory and Linear Programming based on: R 2.10.0, Scilab 5.1.1 and an Open Source Spreadsheet will be illustrated, with a brief reference to the acceptance of pupils and colleagues.
- Superefficiency and Multiplier Adjustment in Data Envelopment AnalysisPublication . Santos, Jorge M. A.; Santos, Luís Cavique; Mendes, Armando B.Superefficiency is an important extension of DEA that overcomes some limitations of the traditional models, specifically allowing ranking of efficient units and a unique set of weights for those units. Weights restriction is a well-known technique in the DEA field. When those techniques are applied, weights cluster around its new limits, making its evaluation dependent of its levels. This chapter introduces a new approach to weights adjustment by goal programming techniques, avoiding the imposition of hard restrictions that can even lead to unfeasibility. This method results in models that are more flexible.