Mining Periodic-Frequent Itemsets with Approximate Periodicity Using Interval Transaction-Ids List Tree

Temporal periodicity of itemset appearance can be regarded as an important criterion for measuring the interestingness of itemsets in several application. A frequent itemset can be said periodic-frequent in a database if it appears at a regular interval given by the user. In this paper, we propose a concept of the approximate periodicity of each itemset. Moreover, a new tree-based data structure, called ITL-tree (Interval Transaction-ids List tree), is proposed. Our tree structure maintains an approximation of the occurrence information in a highly compact manner for the periodic-frequent itemsets mining. A pattern-growth mining is used to generate all of periodic-frequent itemsets by a bottom-up traversal of the ITL-tree for user-given periodicity and support thresholds. The performance study shows that our data structure is very efficient for mining periodic-frequent itemsets with approximate periodicity results.