TKFIM: Top-K frequent itemset mining technique based on equivalence classes.

ABSTRACT: Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset's characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent ite

SUBMITTER: Iqbal S 

PROVIDER: S-EPMC7959650 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature