Empirical Evaluation of Bit Mask Search for Mining Frequent Item Sets
Jayshree Boaddh, Urmila Mahor, Niket Bhargava · 2012
The previous bit-search technique does not provide any compaction or compression mechanism the density in bit- vector regions. As a result, on the sparse dataset only one or two bits are set in each bit -search region, which not only increase the projection length but also it is not possible to achieve true 32bit CPU performance. To increase the density in bit-vector regions the Bit Search Mask Search starts with an array list. From root node, a bit search mask search representation for each frequent item is created which gives a sufficient compression and compaction in bit search. In this paper we can find sufficient improvements in Bit search Mask Search by using this approach.