Rastro-DM: data mining with a trail
Marcus Vinicius Borela de Castro Remis Balaniuk
This paper proposes a methodology for documenting data mining (DM) projects,
Rastro-DM (Trail Data Mining), with a focus not on the model that is generated,
but on the processes behind its construction, in order to leave a trail (Rastro
in Portuguese) of planned actions, training completed, results obtained, and
lessons learned. The proposed practices are complementary to structuring
methodologies of DM, such as CRISP-DM, which establish a methodological and
paradigmatic framework for the DM process. The application of best practices
and their benefits is illustrated in a project called 'Cladop' that was created
for the classification of PDF documents associated with the investigative
process of damages to the Brazilian Federal Public Treasury. Building the
Rastro-DM kit in the context of a project is a small step that can lead to an
institutional leap to be achieved by sharing and using the trail across the
enterprise.