Abstract Association Rule Mining (ARM) is a significant task for discovering frequent patterns
in data mining. It has achieved great success in a plethora of applications such as market …

Abstract Exact approaches to Frequent Itemsets Mining (FIM) are characterised by poor
runtime performance when dealing with large database instances. Several FIM bio-inspired …

This paper investigates the use of GPU (Graphics Processing Unit) in improving the bees
swarm optimization metaheuristic performance for solving the association rule mining …

In the context of big data, many scientific communities aim to provide efficient approaches to
accommodate large-scale datasets. This is the case of the machine-learning community …

Association rule mining (ARM) is one of the most important tasks in data mining. In recent
years, swarm intelligence algorithms have been effectively applied to ARM, and the main …

The quest for frequent itemsets in a transactional database is explored in this paper, for the
purpose of extracting hidden patterns from the database. Two major limitations of the Apriori …

This paper proposes a novel framework for metaheuristic-based Frequent Itemset Mining
(FIM), which considers intrinsic features of the FIM problem. The framework, called META …

Our-days, with the significant number of connected devices, data stored has grown
significantly. The exploitation of the information stored in it for decision-making has become …

An effective data mining method to automatically extract association rules between
manufacturing capabilities and product features from the available historical data is …

An effective data mining method to automatically extract association rules between
manufacturing capabilities and product features from the available historical data is …