10 resultados para Association rules
em Instituto Politécnico do Porto, Portugal
Resumo:
Many current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulnerable to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality.
Resumo:
Ao longo dos últimos anos, as regras de associação têm assumido um papel relevante na extracção de informação e de conhecimento em base de dados e vêm com isso auxiliar o processo de tomada de decisão. A maioria dos trabalhos de investigação desenvolvidos sobre regras de associação têm por base o modelo de suporte e confiança. Este modelo permite obter regras de associação que envolvem particularmente conjuntos de itens frequentes. Contudo, nos últimos anos, tem-se explorado conjuntos de itens que surgem com menor frequência, designados de regras de associação raras ou infrequentes. Muitas das regras com base nestes itens têm particular interesse para o utilizador. Actualmente a investigação sobre regras de associação procuram incidir na geração do maior número possível de regras com interesse aglomerando itens raros e frequentes. Assim, este estudo foca, inicialmente, uma pesquisa sobre os principais algoritmos de data mining que abordam as regras de associação. A finalidade deste trabalho é examinar as técnicas e algoritmos de extracção de regras de associação já existentes, verificar as principais vantagens e desvantagens dos algoritmos na extracção de regras de associação e, por fim, desenvolver um algoritmo cujo objectivo é gerar regras de associação que envolvem itens raros e frequentes.
Resumo:
Atualmente, são geradas enormes quantidades de dados que, na maior parte das vezes, não são devidamente analisados. Como tal, existe um fosso cada vez mais significativo entre os dados existentes e a quantidade de dados que é realmente analisada. Esta situação verifica-se com grande frequência na área da saúde. De forma a combater este problema foram criadas técnicas que permitem efetuar uma análise de grandes massas de dados, retirando padrões e conhecimento intrínseco dos dados. A área da saúde é um exemplo de uma área que cria enormes quantidades de dados diariamente, mas que na maior parte das vezes não é retirado conhecimento proveitoso dos mesmos. Este novo conhecimento poderia ajudar os profissionais de saúde a obter resposta para vários problemas. Esta dissertação pretende apresentar todo o processo de descoberta de conhecimento: análise dos dados, preparação dos dados, escolha dos atributos e dos algoritmos, aplicação de técnicas de mineração de dados (classificação, segmentação e regras de associação), escolha dos algoritmos (C5.0, CHAID, Kohonen, TwoSteps, K-means, Apriori) e avaliação dos modelos criados. O projeto baseia-se na metodologia CRISP-DM e foi desenvolvido com a ferramenta Clementine 12.0. O principal intuito deste projeto é retirar padrões e perfis de dadores que possam vir a contrair determinadas doenças (anemia, doenças renais, hepatite, entre outras) ou quais as doenças ou valores anormais de componentes sanguíneos que podem ser comuns entre os dadores.
Resumo:
Dissertação apresentada ao Instituto Superior de Contabilidade para obtenção do Grau de Mestre em Auditoria Orientada por: Doutora Alcina Dias
Resumo:
The development of neonatal intensive care has led to an increase in the prevalence of children with low birth weight and associated morbidity. The objectives of this study are to verify (1) The association between birth weight (BW) and neuromotor performance? (2) Is the neuromotor performance of twins within the normal range? (3) Are intra-pair similarities in neuromotor development of Monozygotic (MZ) and Disygotic (DZ) twins of unequal magnitude? The sample consisted of 191 children (78 MZ and 113 DZ), 8.9+3.1 years of age and with an average BW of 2246.3+485.4g. In addition to gestational characteristics, sports participation and Zurich Neuromotor Assessment (ZNA) were observed at childhood age. The statistical analysis was carried out with software SPSS 18.0, the STATA 10 and the ZNA performance scores. The level of significance was 0.05. For the neuromotor items high intra and inter-investigator reliabilities were obtained (0.793
Resumo:
For integer-order systems, there are well-known practical rules for RL sketching. Nevertheless, these rules cannot be directly applied to fractional-order (FO) systems. Besides, the existing literature on this topic is scarce and exclusively focused on commensurate systems, usually expressed as the ratio of two noninteger polynomials. The practical rules derived for those do not apply to other symbolic expressions, namely, to transfer functions expressed as the ratio of FO zeros and poles. However, this is an important case as it is an extension of the classical integer-order problem usually addressed by control engineers. Extending the RL practical sketching rules to such FO systems will contribute to decrease the lack of intuition about the corresponding system dynamics. This paper generalises several RL practical sketching rules to transfer functions specified as the ratio of FO zeros and poles. The subject is presented in a didactic perspective, being the rules applied to several examples.
Resumo:
Dissertação de Mestrado apresentada ao Instituto Superior de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Auditoria sob orientação de Mestre Helena Maria Santos de Oliveira
Resumo:
The integration of growing amounts of distributed generation in power systems, namely at distribution networks level, has been fostered by energy policies in several countries around the world, including in Europe. This intensive integration of distributed, non-dispatchable, and natural sources based generation (including wind power) has caused several changes in the operation and planning of power systems and of electricity markets. Sometimes the available non-dispatchable generation is higher than the demand. This generation must be used; otherwise it is wasted if not stored or used to supply additional demand. New policies and market rules, as well as new players, are needed in order to competitively integrate all the resources. The methodology proposed in this paper aims at the maximization of the social welfare in a distribution network operated by a virtual power player that aggregates and manages the available energy resources. When facing a situation of excessive non-dispatchable generation, including wind power, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. This method is especially useful when actual and day-ahead resources forecast differ significantly. The distribution network characteristics and concerns are addressed by including the network constraints in the optimization model. The proposed methodology has been implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20.310 consumers and 548 distributed generators, some of them non-dispatchable and with must take contracts. The implemented scenario corresponds to a real day in Portuguese power system.
Resumo:
BACKGROUND: Some studies have reported an inverse association between dairy product (DP) consumption and weight or fat mass loss. OBJECTIVES: The objective of our study was to assess the association between DP intake and abdominal obesity (AO) among Azorean adolescents. SUBJECTS/METHODS: This study was a cross-sectional analysis. A total of 903 adolescents (370 boys) aged 15--16 years was evaluated. Anthropometric measurements were collected (weight, height and waist circumference (WC)) and McCarthy’s cut-points were used to categorize WC. AO was defined when WC was X90th percentile. Adolescent food intake was assessed using a self-administered semiquantitative food frequency questionnaire and DP intake was categorized in o2 and X2 servings/day. Data were analyzed separately for girls and boys, and logistical regression was used to estimate the association between DPs and AO adjusting for potential confounders. RESULTS: The prevalence of AO was 54.9% (boys: 32.1% and girls: 70.7%, Po0.001). For boys and girls, DP consumption was 2.3±1.9 and 2.1±1.6 servings/day (P¼0.185), respectively. In both genders, the proportion of adolescents with WC o90th percentile was higher among individuals who reported a dairy intake of X2 servings/day compared with those with an intake o2 servings/day (boys: 71% vs 65% and girls: 36% vs 24%, Po0.05). After adjustments for confounders, two or more DP servings per day were a negative predictor of AO (odds ratio, 0.217; 95% confidence interval, 0.075 -- 0.633) only in boys. CONCLUSION: We found a protective association between DP intake and AO only in boys.
Resumo:
In this paper, a rule-based automatic syllabifier for Danish is described using the Maximal Onset Principle. Prior success rates of rule-based methods applied to Portuguese and Catalan syllabification modules were on the basis of this work. The system was implemented and tested using a very small set of rules. The results gave rise to 96.9% and 98.7% of word accuracy rate, contrary to our initial expectations, being Danish a language with a complex syllabic structure and thus difficult to be rule-driven. Comparison with data-driven syllabification system using artificial neural networks showed a higher accuracy rate of the former system.