IIS-Mine: A new efficient method for mining frequent itemsets

A new approach to mine all frequent itemsets from a transaction database isproposed. The main features of this paper are as follows: (1) the proposed algorithmperforms database scanning only once to construct a data structure called an invertedindex structure (IIS); (2) the change in the minimum support threshold is not affected bythis structure, and as a result, a rescan of the database is not required; and (3) theproposed mining algorithm, IIS-Mine, uses an efficient property of an extendable itemset,which reduces the recursiveness of mining steps without generating candidate itemsets,allowing frequent itemsets to be found quickly. We have provided definitions, examples,and a theorem, the completeness and correctness of which is shown by mathematicalproof. We present experiments in which the run time, memory consumption and scalabilityare tested in comparison with a frequent-pattern (FP) growth algorithm when theminimum support threshold is varied. Both algorithms are evaluated by applying them tosynthetics and real-world datasets. The experimental results demonstrate that IIS-Mineprovides better performance than FP-growth in terms of run time and space consumptionand is effective when used on dense datasets.