Discovering Periodic-Frequent Patterns in Transactional Databases

Syed Khairuzzaman Tanbeer, Chowdhury Farhan Ahmed, Byeong-Soo Jeong, Young-Koo Lee

Abstract: Since mining frequent patterns from transactional databases involves an exponential mining space and generates a huge number of patterns, efficient discovery of user-interest-based frequent pattern set becomes the first priority for a mining algorithm. In many real-world scenarios it is often sufficient to mine a small interesting representative subset of frequent patterns. Temporal periodicity of pattern appearance can be regarded as an important criterion for measuring the interestingness of frequent patterns in several applications. A frequent pattern can be said periodic-frequent if it appears at a regular interval given by the user in the database. In this paper, we introduce a novel concept of mining periodic-frequent patterns from transactional databases. We use an efficient tree-based data structure, called Periodic-frequent pattern tree (PF-tree in short), that captures the database contents in a highly compact manner and enables a pattern growth mining technique to generate the complete set of periodic-frequent patterns in a database for user-given periodicity and support thresholds. The performance study shows that mining periodic-frequent patterns with PF-tree is time and memory efficient and highly scalable as well.