  Mining frequent itemset using bit-vector representation approach is very
efficient for dense type datasets, but highly inefficient for sparse datasets
due to lack of any efficient bit-vector projection technique. In this paper we
present a novel efficient bit-vector projection technique, for sparse and dense
datasets. To check the efficiency of our bit-vector projection technique, we
present a new frequent itemset mining algorithm Ramp (Real Algorithm for Mining
Patterns) build upon our bit-vector projection technique. The performance of
the Ramp is compared with the current best (all, maximal and closed) frequent
itemset mining algorithms on benchmark datasets. Different experimental results
on sparse and dense datasets show that mining frequent itemset using Ramp is
faster than the current best algorithms, which show the effectiveness of our
bit-vector projection idea. We also present a new local maximal frequent
itemsets propagation and maximal itemset superset checking approach FastLMFI,
build upon our PBR bit-vector projection technique. Our different computational
experiments suggest that itemset maximality checking using FastLMFI is fast and
efficient than a previous will known progressive focusing approach.
