Discovering Knowledge from Fuzzy Concept Lattice
Sadok Ben Yahia, Ali Jaoua · Studies in fuzziness and soft computing · 2001
Since its inception, association rule mining has become one of the core data mining tasks, and has attracted tremendous interest among researchers and practitioners. Many efficient algorithms have been proposed in the literature, e.g., Apriori, Partition, DIC, for mining association rules in the context of marketbasket analysis. They are all based on apriori methods, i.e., pruning the itemset lattice, and requires multiple database accesses. However, research so far has mainly focused on mining over binary data, i.e., either an item is present in a transaction or not. Little attention was paid to mining over data where the quantity of items is considered. In this paper, we propose to address the problem of mining fuzzy association rules, by considering the quantity of items in the transactions. After the fuzzification of the transaction database, we apply a new efficient algorithm, called FARD (Fuzzy Association Rule Discovery), for mining fuzzy association rules. FARD is based on the pruning of the fuzzy concept lattice, and can be applied equally to classical or fuzzy databases, by scanning the database only once.