Quick mining in dense data: applying probabilistic support prediction in depth-first order.

ABSTRACT: Frequent itemset mining (FIM) is a major component in association rule mining, significantly influencing its performance. FIM is a computationally intensive nondeterministic polynomial time (NP)-hard problem. At the core of FIM is the task of computing support of candidate itemsets. This problem becomes more severe when the dataset is dense as the support is computed for millions, or even billions, of candidate itemsets. The rapid growth of data further exacerbates this problem. To achieve high scalability and efficiency, recently, researchers have proposed various approaches to approximate the support of an itemset using as small a subset of transaction data as possible. In addition to efficiency, accuracy is another important metric for these algorithms. They strive to increase true posi

SUBMITTER: Sadeequllah M 

PROVIDER: S-EPMC11623086 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature