Association rule mining (ARM) aims to find out association rules that satisfy predefined
minimum support and confidence from a given database. However, in many cases ARM …

Association rule mining is an effective data mining technique which has been used widely in
health informatics research right from its introduction. Since health informatics has received …

This study proposes a methodology for market basket analysis based on minimum spanning
trees, which complements the search for significant association rules among the vast set of …

In recent years, weighted frequent itemsets mining (WFIM) has become a critical issue of
data mining, which can be used to discover more useful and interesting patterns in real …

Discovering interesting and useful association rules from the collected data is an issue of
great importance in pattern mining. Although a myriad of association rules can be extracted …

Among the inherently interpretable learning algorithms are associative classifiers, which are
induced in steps. Regarding the ranking step, it is carried out using objective measures in …

Over the last years, recommendation techniques have emerged to cope with the challenging
task of optimal service selection, and to help consumers satisfy their needs and preferences …

In knowledge discovery studies, association rules mining has been extensively studied to
discover hidden knowledge and relationships among set of items in a transactional dataset …

Abstract Assertion-Based Verification (ABV) is one of the promising ways of functional
verification. The efficiency of ABV largely depends on the quality of the assertions in terms of …

The widening gap between known proteins and their functions has encouraged the
development of methods to automatically infer annotations. Automatic functional annotation …