Performance Analysis of Frequent Itemset Mining Using Hybrid Database Representation Approach

Frequent itemset mining is considered to an important research oriented task in data mining, due to its large applicability in real world applications. In recent years lot of algorithms and techniques are proposed for enumerating itemsets from transactional databases. In which some are best for dense type datasets, while some are best for sparse type datasets. Currently there is no single algorithm exist that is best for all type of datasets (sparse as well as dense). The main limitation of previous algorithm is that, they depend upon single approach and do not combine the best features of multiple approaches for speedup the process of itemset mining. In this paper, the authors first compare and contract the two main itemset mining strategies on different itemset mining factors, scalability of algorithm, item search order, dataset projection and itemset frequency counting. Then the authors introduce a new hybrid strategy that combines the best features of existing strategies. Our different experiments on benchmark datasets show that mining all and maximal frequent itemsets using hybrid approach outperforms the previous algorithms on almost all types of dense and sparse datasets, which shows the effectiveness of our approach.