Mining fuzzy association rules is the task of finding the fuzzy itemsets which frequently occur together in large fuzzy dataset, but most proposed methods may identify a fuzzy rule with two fuzzy itemsets as interesting when, in fact, the presence of one fuzzy itemsets in a record does not imply the presence of the other one in the same record. To prevent generating this kind of misleading fuzz...

This work presents unaddressed issues in the field of Association Rule Mining (ARM). Looking at the previous literature of varied areas and applications of ARM, we identify three broad categories of ARM where the research is still in the nascent stage. We review papers in the three categories of fuzzy association rules, multilevel association rules and negative association rules to study the st...

Mining transaction databases for association rules usually generates a large number of rules, most of which are unnecessary when used for subsequent prediction. In this paper we define a rule set for a given transaction database that is much smaller than the association rule set but makes the same predictions as the association rule set by the confidence priority. We call this subset the inform...

One of the most well-studied problems in data mining is mining for association rules. There was also research that introduced association rule mining methods to conduct classification tasks. These classification methods, based on association rule mining, could be applied for customer segmentation. Currently, most of the association rule mining methods are based on a supportconfidence structure,...