Obtaining optimal quality measures for quantitative association rules

There exist several works in the literature in which fitness functions based on a combination of weighted measures for the discovery of association rules have been proposed. Nevertheless, some differences in the measures used to assess the quality of association rules could be obtained according to the values of the weights of the measures included in the fitness function. Therefore, user's decision is very important in order to specify the weights of the measures involved in the optimization process. This paper presents a study of well-known quality measures with regard to the weights of the measures that appear in a fitness function. In particular, the fitness function of an existing evolutionary algorithm called QARGA has been considered with the purpose of suggesting the values that should be assigned to the weights, depending on the set of measures to be optimized. As initial step, several experiments have been carried out from 35 public datasets in order to show how the weights for confidence, support, amplitude and number of attributes measures included in the fitness function have an influence on different quality measures according to several minimum support thresholds. Second, statistical tests have been conducted for evaluating when the differences in measures of the rules obtained by QARGA are significative, and thus, to provide the best weights to be considered depending on the group of measures to be optimized. Finally, the results obtained when using the recommended weights for two real-world applications related to ozone and earthquakes are reported.Ministerio de Ciencia y Tecnología TIN2011-28956-C02Junta de Andalucía P12- TIC-1728Universidad Pablo de Olavide APPB81309

Discovery of Genes Implied in Cancer by Genetic Algorithms and Association Rules

This work proposes a methodology to identify genes highly related with cancer. In particular, a multi-objective evolutionary algo rithm named CANGAR is applied to obtain quantitative association rules. This kind of rules are used to identify dependencies between genes and their expression levels. Hierarchical cluster analysis, fold-change and review of the literature have been considered to validate the relevance of the results obtained. The results show that the reported genes are consistent with prior knowledge and able to characterize cancer colon patients.Ministerio de Ciencia y Tecnología TIN2011-28956-C02-02Ministerio de Economía y Competitividad TIN2014-55894-C2-1-RJunta de Andalucía P11-TIC-7528Junta de Andalucía P12-TIC-172

Mineração de regras de associação diversas em dados meteorológicos temporais de múltiplos pontos geográficos via algoritmo genético / Mining of diverse association rules in temporal meteorological data from multiple geographic points via genetic algorithm

O conhecimento das associações de fatores climáticos que influenciam o clima em uma determinada região é importante para análises climáticas e planejamentos de curto a longo prazo. Contudo, os métodos tradicionais existentes na literatura para a descoberta de associações apresentam várias deficiências como alto custo computacional, o que impede sua aplicação até mesmo para conjunto de dados relativamente pequenos, ajuste de vários parâmetros críticos como limiares de suporte e confiança das regras, além de muitas vezes produzirem regras triviais. Tendo em vista esta limitação dos métodos tradicionais da literatura este artigo usa da teoria de Algoritmos Genéticos e suas extensões (memória Tabu e técnica de Nicho, a saber Clearing) para desenvolver e experimentar metodologias para mineração de regras de associação de dados temporais quantitativos. Os métodos foram aplicados a dados meteorológicos temporais de múltiplas cidades brasileiras para minerar implicações meteorológicas de um conjunto de cidades na situação meteorológica posterior em um cidade específica.  Os experimentos realizados mostram que um dos métodos desenvolvidos que combina memória Tabu e Clearing, é bastante promissor, pois minera uma grande quantidade de regras de alta diversidade e não apresenta problema de convergência

Industrial Symbiosis Recommender Systems

For a long time, humanity has lived upon the paradigm that the amounts of natural resources are unlimited and that the environment has ample regenerative capacity. However, the notion to shift towards sustainability has resulted in a worldwide adoption of policies addressing resource efficiency and preservation of natural resources.One of the key environmental and economic sustainable operations that is currently promoted and enacted in the European Union policy is Industrial Symbiosis. In industrial symbiosis, firms aim to reduce the total material and energy footprint by circulating traditional secondary production process outputs of firms to become part of an input for the production process of other firms.This thesis directs attention to the design considerations for recommender systems in the highly dynamic domain of industrial symbiosis. Recommender systems are a promising technology that may facilitate in multiple facets of the industrial symbiosis creation as they reduce the complexity of decision making. This typical strength of recommender systems has been responsible for improved sales and a higher return of investments. That provides the prospect for industrial symbiosis recommenders to increase the number of synergistic transactions that reduce the total environmental impact of the process industry in particular