  Decision-support systems benefit from hidden patterns extracted from digital
information. In the specific domain of gastropod characterization,
morphometrical measurements support biologists in the identification of land
snail specimens. Although snails can be easily identified by their excretory
and reproductive systems, the after-death mollusk body is commonly inaccessible
because of either soft material deterioration or fossilization. This study aims
at characterizing Brazilian land snails by morphometrical data features
manually taken from the shells. In particular, we examined a dataset of shells
by using different learning models that labeled snail specimens with a
precision up to 97.5% (F1-Score = .975, CKC = .967 and ROC Area = .998). The
extracted patterns describe similarities and trends among land snail species
and indicates possible outliers physiologies due to climate traits and
breeding. Finally, we show some morphometrical characteristics dominate others
according to different feature selection biases. Those data-based patterns can
be applied to fast land snail identification whenever their bodies are
unavailable, as in the recurrent cases of lost shells in nature or private and
museum collections.
