  Real world datasets are sparse, dirty and contain hundreds of items. In such
situations, discovering interesting rules (results) using traditional frequent
itemset mining approach by specifying a user defined input support threshold is
not appropriate. Since without any domain knowledge, setting support threshold
small or large can output nothing or a large number of redundant uninteresting
results. Recently a novel approach of mining only N-most/Top-K interesting
frequent itemsets has been proposed, which discovers the top N interesting
results without specifying any user defined support threshold. However, mining
interesting frequent itemsets without minimum support threshold are more costly
in terms of itemset search space exploration and processing cost. Thereby, the
efficiency of their mining highly depends upon three main factors (1) Database
representation approach used for itemset frequency counting, (2) Projection of
relevant transactions to lower level nodes of search space and (3) Algorithm
implementation technique. Therefore, to improve the efficiency of mining
process, in this paper we present two novel algorithms called (N-MostMiner and
Top-K-Miner) using the bit-vector representation approach which is very
efficient in terms of itemset frequency counting and transactions projection.
In addition to this, several efficient implementation techniques of N-MostMiner
and Top-K-Miner are also present which we experienced in our implementation.
Our experimental results on benchmark datasets suggest that the NMostMiner and
Top-K-Miner are very efficient in terms of processing time as compared to
current best algorithms BOMO and TFP.
