Association rule mining (ARM) is a commonly encountred data mining method. There are
many approaches to mining frequent rules and patterns from a database and one among …

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 …

In this paper, we propose QUANTMINER, a mining quantitative association rules system.
This system is based on a genetic algorithm that dynamically discovers “good” intervals in …

Most of the algorithms for mining quantitative association rules focus on positive
dependencies without paying particular attention to negative dependencies. The latter may …

Evolutionary algorithms are normally applied to mine association rules on quantitative data
but most of them obtain enough similar rules due to that the usual behavior of these …

In the vast field of data mining, the increasing significance of Numerical Association Rule
Mining (NARM) lies in its capacity to unearth recurrent patterns and correlations across …

In this paper, we address the problem of asset performance monitoring, with the intention of
both detecting any potential reliability problem and predicting any loss of energy …

Some researchers have framed the extraction of association rules as a multi-objective
problem, jointly optimizing several measures to obtain a set with more interesting and …

Association rule mining is a popular data mining task, which has important in many domains.
Because the task of association rule mining is very time consuming, evolutionary and swarm …