NovaIntell - projecto de text-Mining para a l��ngua portuguesa numa empresa de Gest��o de Informa����o e Conhecimento

Disserta����o apresentada como requisito parcial para obten����o do grau de Mestre em Estat��stica e Gest��o de Informa����o

Using data mining with time series data in short-term stocks prediction

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.

SEROPREVALENCE OF T. cruzi INFECTION IN BLOOD DONORS AND CHAGAS CARDIOMYOPATHY IN PATIENTS FROM THE COAL MINING REGION OF COAHUILA, MEXICO

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.

Building clusters for CRM strategies by mining airlines customer data

Trabalho de Projeto apresentado como requisito parcial para obten����o do grau de Mestre em Estat��stica e Gest��o de Informa����o

Text Mining: an��lise de sentimentos na classifica����o de not��cias

Trabalho de Projeto apresentado como requisito parcial para obten����o do grau de Mestre em Estat��stica e Gest��o de Informa����o

ArcelorMittal: Metals & Mining: Equity Investment Report

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

ArcelorMittal: Metals & Mining ��� Europe: Equity Investment Report

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

Environmental remediation project of Cunha Baixa mining area cost benefit analysis

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

Automatic information retrieval through text-mining

The dissertation presented for obtaining the Master���s Degree in Electrical Engineering and Computer Science, at Universidade Nova de Lisboa, Faculdade de Ci��ncias e Tecnologia

Complex networks and data mining: toward a new perspective for the understanding of complex systems

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.

Text mining e twitter : o poder das redes sociais num mercado competitivo

Actualmente, com a massifica����o da utiliza����o das redes sociais, as empresas passam a sua mensagem nos seus canais de comunica����o, mas os consumidores d��o a sua opini��o 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 utiliz��mos um algoritmo de Clustering hier��rquico com o objectivo de descobrir temas distintos num conjunto de tweets obtidos ao longo de um determinado per��odo de tempo para as empresas Burger King e McDonald���s. Com o intuito de compreender o sentimento associado a estes temas foi feita uma an��lise de sentimentos a cada tema encontrado, utilizando um algoritmo Bag-of-Words. Concluiu-se que o algoritmo de Clustering foi capaz de encontrar temas atrav��s do tweets obtidos, essencialmente ligados a produtos e servi��os comercializados pelas empresas. O algoritmo de Sentiment Analysis atribuiu um sentimento a esses temas, permitindo compreender de entre os produtos/servi��os identificados quais os que obtiveram uma polaridade positiva ou negativa, e deste modo sinalizar potencias situa����es problem��ticas na estrat��gia das empresas, e situa����es positivas pass��veis de identifica����o de decis��es operacionais bem-sucedidas.

Sa��de em Portugal : estudo das urg��ncias hospitalares atrav��s do Data Mining

Qualquer assunto relacionado com a sa��de �� sempre um tema sens��vel, pela import��ncia que tem junto da popula����o, j�� que interage diretamente com o bem-estar das pessoas e, essencialmente, com a sensa����o de seguran��a que as estas pretendem ter na presta����o dos cuidados b��sicos de sa��de. Dados estat��sticos mostram que a popula����o est�� cada vez mais envelhecida, refor��ando a import��ncia da exist��ncia de bons centros hospitalares e de um bom Sistema Nacional de Sa��de (SNS) (Plano Nacional de Sa��de, 2010). Em Portugal, caso os pacientes necessitem de cuidados mais urgentes, podem recorrer ao Servi��o de Urg��ncias disponibilizado para toda a popula����o atrav��s do SNS. No entanto, a gest��o e planeamento deste servi��o �� complexa, dado este servi��o ser frequentemente utilizado por pacientes que n��o necessitam de cuidados urgentes, levando a que os hospitais deixem de conseguir dar a resposta esperada, implicando a presta����o por vezes um servi��o de menor qualidade. Neste sentido, analisaram-se dados de um hospital do norte do pa��s com o intuito de perceber o ponto de situa����o das urg��ncias, de forma a encontrar padr��es relevantes atrav��s da an��lise de clusters e de regras de associa����o. Come��ando pela an��lise de clusters, utilizaram-se apenas as vari��veis que foram consideradas importantes para o problema, resultando da an��lise final 3 clusters. O primeiro cluster �� constitu��do 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 semelhan��as entre os clusters 1 e 3, pois ambos continham os pacientes mais idosos, havendo um padr��o comum no seu comportamento. No ano 2012 n��o houve registo de nenhuma epidemia, n��o havendo por isso nenhuma doen��a que se destacasse comparativamente ��s restantes. Concluiu-se tamb��m que na maior parte dos casos houve a necessidade de uma interven����o urgente (pulseira de cor Amarela), no entanto a maioria dos pacientes observados conseguiu regressar ��s suas habita����es ap��s as consultas nas Urg��ncias Hospitalares, sem interven����es m��dicas adicionais. Relativamente ��s regras de associa����o, houve a necessidade de transformar e eliminar algumas vari��veis que enviesassem o estudo. Ap��s o processo da cria����o das regras de associa����o, percebeu-se que as regras eram muito similares entre si, apresentando uma maior confian��a nas vari��veis que apareceram em maior n��mero (���Pacientes com pulseira de cor Amarela���, ���distrito do Porto��� ou ���Alta M��dica para a Resid��ncia���).

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

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, Arti���cial 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 coe���cient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three in���uential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge con���rmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Business intelligence in banking: A literature analysis from 2002 to 2013 using Text Mining and latent Dirichlet allocation

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.