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Context: Information is currently one of the most important assets of organizations, but it is necessary to transform data into knowledge. Data mining (DM) plays an important role in information analysis and pattern discovery. In this paper we used DM techniques with the help of WEKA software for attribute selection, classification and information grouping of projects of the IFF Innovation Hub. Objective: To identify standards within the data set of innovation projects carried out by IFF in order to generate knowledge and optimize future project management. Methodology: Initially, information was extracted from the project database of the Campos dos Goytacazes Innovation Hub. Then, mining-relevant attributes were selected, with data adequacy for better results. Finally, the decision tree classification and generation algorithm was executed, which allowed revealing information from previously hidden projects. Result: The results described in the various branches of the decision tree vary in terms of user profiles, task types, and time spent. Iterations are revealed between specific user profiles, such as Manager / Coordinator and Scholars / Interns, organization of Researchers' own authoring activities and possible change in the development task list, pointing to many activities without time recording. Conclusions: After the selection of attributes and generation of decision trees, it was possible to interpret and describe the relationships between task attributes, revealing information about the projects. The results can be compared with existing project artifacts or as input to new projects from the project office. New knowledge discoveries in the same database can be tried if new scenarios are applied and new algorithms such as K-means. Key-words: Data Base; Project Management; j48; Data mining; Weka.

DESCOBERTA DE CONHECIMENTO EM BASE DE DADOS DE PROJETOS DE INOVAÇÃO: UM ESTUDO EM UM POLO EMBRAPI