  The PARAFAC tensor decomposition has enjoyed an increasing success in
exploratory multi-aspect data mining scenarios. A major challenge remains the
estimation of the number of latent factors (i.e., the rank) of the
decomposition, which yields high-quality, interpretable results. Previously, we
have proposed an automated tensor mining method which leverages a well-known
quality heuristic from the field of Chemometrics, the Core Consistency
Diagnostic (CORCONDIA), in order to automatically determine the rank for the
PARAFAC decomposition. In this work we set out to explore the trade-off between
1) the interpretability/quality of the results (as expressed by CORCONDIA), and
2) the predictive accuracy of the results, in order to further improve the rank
estimation quality. Our preliminary results indicate that striking a good
balance in that trade-off benefits rank estimation.
