Encontrar pessoas com interesses semelhantes dentro de um domínio pode fornecer um importante auxílio na gestão de centros de investigação. Como a produção académica é facilmente obtida em bases de dados bibliográficas e académicas, estas podem ser usadas para descobrir as afinidades entre os investigadores que não estejam já evidenciadas pela co-autoria. Este processo de descoberta dá-se com a ajuda de técnicas de análise de texto, na base dos termos utilizados nos respectivos documentos. A afinidade pode ser representada em forma de rede, em que os nós representam os artigos de cada investigador e as ligações representam similaridade entre os diferentes investigadores. Cada nó pode ser caracterizado através de diversas medidas de centralidade na rede e algoritmos de detecção de comunidades permitem identificar grupos com interesses semelhantes. Cada nó é ainda caracterizado por um conjunto de palavras-chave e resumos descobertos automaticamente com a ajuda de técnicas avançadas. Este artigo fornece mais detalhes sobre os métodos adoptados e/ou desenvolvidos, alguns dos quais foram implementados no nosso protótipo. Os métodos descritos são gerais e aplicáveis a muitos domínios diferentes, incluindo documentos que descrevem projetos de I&D, documentos associados a legislação, processos judiciais ou procedimentos médicos. Acreditamos deste modo que este trabalho pode ser útil para um público relativamente amplo.
Managers, investors, financial institutions and government agencies have a major concern on forecasting enterprise bankruptcy. It enables the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Throughout the 20th and the 21st century, advances in statistics and computer science fields enabled the development of different trends in financial distress assessment that co-exist today. However, recent Data Mining (DM) techniques are regarded as being the most precise. IT expertise requirements in the constantly evolving DM field may have been a major obstacle to the adoption of these techniques by decision makers. Furthermore, DM software tools that are now widespread offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting the appropriate algorithm. Hence, the adoption of a good workflow method for data processing and analysis is critical for having fast and reliable results. This work presents an overview of the available bankruptcy techniques and provides a comprehensive case study exploring the latest Data Mining techniques.
Forecasting enterprise bankruptcy is a critical area for Business Intelligence. It is a major concern for investors and credit institutions on risk analysis. It may also enable the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Data Mining may deliver a faster and more precise insight about this issue. Widespread software tools offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting that algorithm. Trying to find an answer for this decision in the relatively large amount of available literature in this area with so many options, advantages, and pitfalls may be as informative as distracting. In this chapter, the authors present an empirical study with a comprehensive Knowledge Discovery and Data Mining (KDD) workflow. The proposed classifier selection automation selects an algorithm that has better prediction performance than the most widely documented in the literature.
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