Mining class association rules (CARs) with the itemset constraint is concerned with the
discovery of rules, which contain a set of specific items in the rule antecedent and a class …

Mining class association rules (CARs) is an essential, but time-intensive task in Associative
Classification (AC). A number of algorithms have been proposed to speed up the mining …

Class association rules (CARs) are basically used to build a classification model for
prediction; they can also be used to describe correlations between itemsets and class …

Class association rules play an important role in decision support systems and have thus
been extensively studied. Recently, an efficient algorithm for mining class association rules …

Associative classification (AC) performs much better than other traditional classifiers. It
generates a huge number of class association rules (CARs). Since users are interested in …

This study proposes an enhanced self-adaptive differential evolution based on simulated
annealing for rule extraction (ESADE-Rule). ESADE-Rule adopts IF-THEN as the rule's form …

Pattern discovery does not only end when a process obtained a certain pattern. It also
requires careful evaluation to show whether the pattern is significant enough to support any …

Constraint-based methods for mining class association rules (CARs) have been developed
in recent years. Currently, there are two kinds of constraints including itemset constraints …

In Associative Classification (AC), Class Association Rules are generally used in the
process of classification in the field of medicine, education, business and so on. AC …

Associative classification is a mining technique that integrates classification and association
rule mining for classifying unseen data. Associative classification has been proved that it …