FCILINK: Mining Frequent Closed Itemsets Based on a Link Structure between Transactions

The problem of discovering association rules between items in a database is an emerging area of research. Its goal is to extract significant patterns or interesting rules from large databases. Recent studies of mining association rules have proposed a closure mechanism. It is no longer necessary to mine the set of all of the frequent itemsets and their association rules. Rather, it is sufficient to mine the frequent closed itemsets and their corresponding rules. In the past, a number of algorithms for mining frequent closed itemsets have been based on items. In this paper, we use the transaction itself for mining frequent closed itemsets. An efficient algorithm called FCILINK is proposed that is based on a link structure between transactions. A given database is scanned once and then a much smaller sub-database is scanned twice. Our experimental results show that our algorithm is faster than previously proposed methods. Furthermore, our approach is significantly more efficient for dense databases.