Ramp: Fast Frequent Itemset Mining with Efficient Bit-Vector Projection Technique

TL;DR

This paper introduces Ramp, a novel frequent itemset mining algorithm utilizing an efficient bit-vector projection technique that significantly improves performance on both sparse and dense datasets.

Contribution

The paper presents a new bit-vector projection technique and the Ramp algorithm, enhancing frequent itemset mining efficiency across various dataset types.

Findings

Ramp outperforms existing algorithms on benchmark datasets.

The bit-vector projection technique is effective for both sparse and dense datasets.

FastLMFI improves maximal itemset checking efficiency.

Abstract

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.