An itemset-tree also has the property of being compact.
An itemset-tree is built by inserting a set of transactions into the tree. For example, we could insert the following 6 transactions (t1,t2...t5) into an itemset-tree.
In this example, the transaction t1 represents the set of items .
The operation of the mechanism based on appropriate settings of two support-based measures is examined through experiments.
Results from three real-world data sets show that the proposed approach is efficient and reliable.
Many applications engender colossal amount of operational and behavioral data.
Copious effective algorithms are proposed in the literature for mining frequent itemsets and association rules.
However, they still required the database to be rescanned in some situations.