Hatchery is a very important sector in egg production. As computers become cheaper, there is an increase in data storage for the production management process. Data Mining has appeared as a technique to identify new and useful knowledge in databases. The objective of this work was to explore the Decision Tree technique in hatchery databases to identify the best standards of the incubation process. The data set used in this research was supplied by Hy-Line do Brasil Ltda., corresponding to the incubation period of 2002-2006, from the strain Hy-line W-36. Two experiments were carried out. In the first experiment, values higher than the company's standards for saleable females were identified as relevant to generate the rules. In the second experiment, values below those established by the company were identified as relevant for the generation of rules. Entropy C 4.5 algorithm and the software SAS-Enterprise Miner were used for data analysis. The conclusion is that, with the technique studied, the data used for production management are sufficient to identify new, useful and applicable knowledge in order to increase productivity of hatcheries, catering for the demand with less waste.
Data mining; KDD; artificial intelligence; poultry science
