HybridMiner: Mining Maximal Frequent Itemsets Using Hybrid Database Representation Approach

TL;DR

HybridMiner introduces a hybrid database representation combining array-based and bitmap layouts to efficiently mine maximal frequent itemsets on large, sparse datasets, outperforming previous algorithms.

Contribution

It proposes a novel hybrid database representation and a maximal algorithm that improve scalability and efficiency in mining maximal frequent itemsets.

Findings

Outperforms previous maximal algorithms on real and benchmark datasets.

Demonstrates improved scalability and efficiency.

Effective on sparse, large datasets.

Abstract

In this paper we present a novel hybrid (arraybased layout and vertical bitmap layout) database representation approach for mining complete Maximal Frequent Itemset (MFI) on sparse and large datasets. Our work is novel in terms of scalability, item search order and two horizontal and vertical projection techniques. We also present a maximal algorithm using this hybrid database representation approach. Different experimental results on real and sparse benchmark datasets show that our approach is better than previous state of art maximal algorithms.