Ramp: high performance frequent itemset mining with efficient bit-vector projection technique

Authors:
Shariq Bashir;Abdul Rauf Baig
Affiliations:
FAST-National University of Computer and Emerging Sciences, Islamabad, Pakistan;FAST-National University of Computer and Emerging Sciences, Islamabad, Pakistan
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

Citing 3
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Abstract

Mining frequent itemset using bit-vector representation approach is very efficient for small dense 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. We also present a new frequent itemset mining algorithm Ramp (Real Algorithm for Mining Patterns) using bit-vector representation approach and our bit-vector projection technique. The performance of the Ramp is compared with the current best frequent itemset mining algorithms. Different experimental results on sparse datasets show that mining frequent itemset using Ramp is faster than the current best algorithms.