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 …

Numerical association rule mining is a widely used variant of the association rule mining
technique, and it has been extensively used in discovering patterns and relationships in …

Association rule discovery is an ever increasing area of interest in data mining. Finding rules
for attributes with numerical values is still a challenging point in the process of association …

Association rule mining (ARM) is defined by its crucial role in finding common pattern in data
mining. It has different types such as fuzzy, binary, numerical. In this paper, we introduce a …

Association rule mining is an important data analysis method that can discover associations
within data. There are numerous previous studies that focus on finding fuzzy association …

Real‐time data stream mining algorithms are largely based on binary datasets and do not
handle continuous quantitative data streams, especially in medical data mining field …

Since finding quantitative association rules (QARs) is an NP-hard problem, evolutionary
methods are suitable solutions for discovery QARs. Nevertheless, most of the previous …

We consider the following data summarization problem. We are given a dataset including
ordinal or numeric explanatory attributes and an outcome attribute. We want to produce a …

Although association rule mining is an important pattern recognition and data analysis
technique, extracting and finding significant rules from a large collection has always been …

We present QARMA, an efficient novel parallel algorithm for mining all Quantitative
Association Rules in large multidimensional datasets where items are required to have at …