Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Demand-driven frequent itemset mining using pattern structures
Knowledge and Information Systems
Discovery of maximum length frequent itemsets
Information Sciences: an International Journal
CP-tree: a tree structure for single-pass frequent pattern mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
An efficient algorithm for frequent itemset mining on data streams
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Mining the k-most interesting frequent patterns sequentially
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Towards efficient mining of periodic-frequent patterns in transactional databases
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
An alternative interestingness measure for mining periodic-frequent patterns
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Finding longest approximate periodic patterns
WADS'11 Proceedings of the 12th international conference on Algorithms and data structures
Discovering association rules change from large databases
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Mining top-k regular-frequent itemsets using database partitioning and support estimation
Expert Systems with Applications: An International Journal
Efficient mining top-k regular-frequent itemset using compressed tidsets
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
An efficient approach to mine periodic-frequent patterns in transactional databases
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Efficient mining regularly frequent patterns in transactional databases
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Detecting approximate periodic patterns
MedAlg'12 Proceedings of the First Mediterranean conference on Design and Analysis of Algorithms
Effective periodic pattern mining in time series databases
Expert Systems with Applications: An International Journal
Closeness Preference - A new interestingness measure for sequential rules mining
Knowledge-Based Systems
Discovering diverse-frequent patterns in transactional databases
Proceedings of the 17th International Conference on Management of Data
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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.