Integration and knowledge reuse environment for producing award winning solutions for binary decision data mining problems

This paper has two main objectives. One is presenting a hybrid framework for KDD project development where all tools for KDD project development are integrated. The other is providing an integrated environment for knowledge reuse, for preventing recurrence of known errors, based on previous experience. Different from purely algorithmic papers, this one focuses on performance metrics used for managerial activities such as the time taken for solution development, the amount of files not automatically managed and others, while preserving equivalent performance on the technical solution. This framework has been validated with metadata collected from previous projects developed and deployed for real world applications by the development team members, including public data mining competitions. The case study carried out in actual contracted projects have shown that this framework assesses the risk of failure for new projects, controls and documents all the KDD project development process and helps understanding the conditions that lead KDD projects to success.