Interpretability analysis of fuzzy association rules supported by fingrams
David P. Pancho, José M. Alonso, Jesús Alcalá‐Fdez, Luis Magdalena · 2013
This work extends fuzzy inference-grams (fingrams) to fuzzy association rules (FAR), yielding FAR-Fingrams.Their analysis pays attention to interpretability issues.An important open problem in association rule mining is the huge number of frequent itemsets and interesting rules to uncover and communicate to the user.FAR-Fingrams address such problem through visual analysis.They ease the selection of rules according to the user's preferences and quality criteria.A new metric to construct fingrams is proposed, reflecting the particularities of FAR.Finally, some of the potentials of FAR-Fingrams are overviewed over a real-world problem that deals with user-preferences.