HybridMiner: Mining Maximal Frequent Itemsets Using Hybrid Database Representation Approach
Databases
2016-11-17 v1 Artificial Intelligence
Data Structures and Algorithms
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.
Keywords
Cite
@article{arxiv.0904.3312,
title = {HybridMiner: Mining Maximal Frequent Itemsets Using Hybrid Database Representation Approach},
author = {Shariq Bashir and Abdul Rauf Baig},
journal= {arXiv preprint arXiv:0904.3312},
year = {2016}
}
Comments
8 Pages In the proceedings of 9th IEEE-INMIC 2005, Karachi, Pakistan, 2005