Linguistic association rules

The class of a priori algorithms are popular association rule mining techniques. However, these algorithms are computationally expensive. The authors propose another novel approach to extract association rules. The method represents an itemset information as a cell of a hypercube. The hypercube encodes associations between the items of each transaction. Apart from proposing the main result, we also propose linguistic association rules. Linguistic association rules encode fuzzy information and represent summarized rules.