875 resultados para Mining
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Dissertao apresentada como requisito parcial para obteno do grau de Mestre em Estatstica e Gesto de Informao
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Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on short- time stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.
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Context and Objective: Chagas disease is considered a worldwide emerging disease; it is endemic in Mexico and the state of Coahuila and is considered of little relevance. The objective of this study was to determine the seroprevalence of T. cruzi infection in blood donors and Chagas cardiomyopathy in patients from the coal mining region of Coahuila, Mexico.Design and Setting: Epidemiological, exploratory and prospective study in a general hospital during the period January to June 2011.Methods: We performed laboratory tests ELISA and indirect hemagglutination in three groups of individuals: 1) asymptomatic voluntary blood donors, 2) patients hospitalized in the cardiology department and 3) patients with dilated cardiomyopathy.Results: There were three levels of seroprevalence: 0.31% in asymptomatic individuals, 1.25% in cardiac patients and in patients with dilated cardiomyopathy in 21.14%.Conclusions: In spite of having detected autochthonous cases of Chagas disease, its importance to local public health remains to be established as well as the details of the dynamics of transmission so that the study is still in progress.
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Trabalho de Projeto apresentado como requisito parcial para obteno do grau de Mestre em Estatstica e Gesto de Informao
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Trabalho de Projeto apresentado como requisito parcial para obteno do grau de Mestre em Estatstica e Gesto de Informao
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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA School of Business and Economics
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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA School of Business and Economics
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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA School of Business and Economics
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The dissertation presented for obtaining the Masters Degree in Electrical Engineering and Computer Science, at Universidade Nova de Lisboa, Faculdade de Cincias e Tecnologia
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Complex systems, i.e. systems composed of a large set of elements interacting in a non-linear way, are constantly found all around us. In the last decades, different approaches have been proposed toward their understanding, one of the most interesting being the Complex Network perspective. This legacy of the 18th century mathematical concepts proposed by Leonhard Euler is still current, and more and more relevant in real-world problems. In recent years, it has been demonstrated that network-based representations can yield relevant knowledge about complex systems. In spite of that, several problems have been detected, mainly related to the degree of subjectivity involved in the creation and evaluation of such network structures. In this Thesis, we propose addressing these problems by means of different data mining techniques, thus obtaining a novel hybrid approximation intermingling complex networks and data mining. Results indicate that such techniques can be effectively used to i) enable the creation of novel network representations, ii) reduce the dimensionality of analyzed systems by pre-selecting the most important elements, iii) describe complex networks, and iv) assist in the analysis of different network topologies. The soundness of such approach is validated through different validation cases drawn from actual biomedical problems, e.g. the diagnosis of cancer from tissue analysis, or the study of the dynamics of the brain under different neurological disorders.
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Actualmente, com a massificao da utilizao das redes sociais, as empresas passam a sua mensagem nos seus canais de comunicao, mas os consumidores do a sua opinio sobre ela. Argumentam, opinam, criticam (Nardi, Schiano, Gumbrecht, & Swartz, 2004). Positiva ou negativamente. Neste contexto o Text Mining surge como uma abordagem interessante para a resposta necessidade de obter conhecimento a partir dos dados existentes. Neste trabalho utilizmos um algoritmo de Clustering hierrquico com o objectivo de descobrir temas distintos num conjunto de tweets obtidos ao longo de um determinado perodo de tempo para as empresas Burger King e McDonalds. Com o intuito de compreender o sentimento associado a estes temas foi feita uma anlise de sentimentos a cada tema encontrado, utilizando um algoritmo Bag-of-Words. Concluiu-se que o algoritmo de Clustering foi capaz de encontrar temas atravs do tweets obtidos, essencialmente ligados a produtos e servios comercializados pelas empresas. O algoritmo de Sentiment Analysis atribuiu um sentimento a esses temas, permitindo compreender de entre os produtos/servios identificados quais os que obtiveram uma polaridade positiva ou negativa, e deste modo sinalizar potencias situaes problemticas na estratgia das empresas, e situaes positivas passveis de identificao de decises operacionais bem-sucedidas.
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Qualquer assunto relacionado com a sade sempre um tema sensvel, pela importncia que tem junto da populao, j que interage diretamente com o bem-estar das pessoas e, essencialmente, com a sensao de segurana que as estas pretendem ter na prestao dos cuidados bsicos de sade. Dados estatsticos mostram que a populao est cada vez mais envelhecida, reforando a importncia da existncia de bons centros hospitalares e de um bom Sistema Nacional de Sade (SNS) (Plano Nacional de Sade, 2010). Em Portugal, caso os pacientes necessitem de cuidados mais urgentes, podem recorrer ao Servio de Urgncias disponibilizado para toda a populao atravs do SNS. No entanto, a gesto e planeamento deste servio complexa, dado este servio ser frequentemente utilizado por pacientes que no necessitam de cuidados urgentes, levando a que os hospitais deixem de conseguir dar a resposta esperada, implicando a prestao por vezes um servio de menor qualidade. Neste sentido, analisaram-se dados de um hospital do norte do pas com o intuito de perceber o ponto de situao das urgncias, de forma a encontrar padres relevantes atravs da anlise de clusters e de regras de associao. Comeando pela anlise de clusters, utilizaram-se apenas as variveis que foram consideradas importantes para o problema, resultando da anlise final 3 clusters. O primeiro cluster constitudo por elementos do sexo masculino de todas as idades, o segundo cluster por elementos do sexo masculino mais jovens e por elementos do sexo feminino at aos 60 anos e o terceiro cluster apenas por elementos do sexo feminino a partir dos 40 anos. No final verificaram-se muitas semelhanas entre os clusters 1 e 3, pois ambos continham os pacientes mais idosos, havendo um padro comum no seu comportamento. No ano 2012 no houve registo de nenhuma epidemia, no havendo por isso nenhuma doena que se destacasse comparativamente s restantes. Concluiu-se tambm que na maior parte dos casos houve a necessidade de uma interveno urgente (pulseira de cor Amarela), no entanto a maioria dos pacientes observados conseguiu regressar s suas habitaes aps as consultas nas Urgncias Hospitalares, sem intervenes mdicas adicionais. Relativamente s regras de associao, houve a necessidade de transformar e eliminar algumas variveis que enviesassem o estudo. Aps o processo da criao das regras de associao, percebeu-se que as regras eram muito similares entre si, apresentando uma maior confiana nas variveis que apareceram em maior nmero (Pacientes com pulseira de cor Amarela, distrito do Porto ou Alta Mdica para a Residncia).
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Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Articial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coecient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three inuential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge conrmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.
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telligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of ar- ticles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.