  Maximal frequent patterns superset checking plays an important role in the
efficient mining of complete Maximal Frequent Itemsets (MFI) and maximal search
space pruning. In this paper we present a new indexing approach, FastLMFI for
local maximal frequent patterns (itemset) propagation and maximal patterns
superset checking. Experimental results on different sparse and dense datasets
show that our work is better than the previous well known progressive focusing
technique. We have also integrated our superset checking approach with an
existing state of the art maximal itemsets algorithm Mafia, and compare our
results with current best maximal itemsets algorithms afopt-max and FP
(zhu)-max. Our results outperform afopt-max and FP (zhu)-max on dense (chess
and mushroom) datasets on almost all support thresholds, which shows the
effectiveness of our approach.
