Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Mining confident rules without support requirement
Proceedings of the tenth international conference on Information and knowledge management
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of High Confidience Association Rules without Support Thresholds
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining N-most Interesting Itemsets
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Frequent Itemsets without Support Threshold: With and without Item Constraints
IEEE Transactions on Knowledge and Data Engineering
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
ACM SIGMOD Record
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Short communication: TOPSIS: Finding Top-K significant N-itemsets in sliding windows adaptively
Knowledge-Based Systems
Discovering Periodic-Frequent Patterns in Transactional Databases
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Interactive mining of top-K frequent closed itemsets from data streams
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Efficient incremental mining of top-K frequent closed itemsets
DS'07 Proceedings of the 10th international conference on Discovery science
High confidence association mining without support pruning
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Mining non-coincidental rules without a user defined support threshold
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining top-k frequent closed itemsets over data streams using the sliding window model
Expert Systems with Applications: An International Journal
Efficient computation of frequent and top-k elements in data streams
ICDT'05 Proceedings of the 10th international conference on Database Theory
Misleading Generalized Itemset discovery
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Temporal regularity of itemset appearance can be regarded as an important criterion for measuring the interestingness of itemsets in several applications. A frequent itemset can be said to be regular-frequent in a database if it appears at a regular period. Therefore, the problem of mining a complete set of regular-frequent itemsets requires the specification of a support and a regularity threshold. However, in practice, it is often difficult for users to provide an appropriate support threshold. In addition, the use of a support threshold tends to produce a large number of regular-frequent itemsets and it might be better to ask for the number of desired results. We thus propose an efficient algorithm for mining top-k regular-frequent itemsets without setting a support threshold. Based on database partitioning and support estimation techniques, the proposed algorithm also uses a best-first search strategy with only one database scan. We then compare our algorithm with the state-of-the-art algorithms for mining top-k regular-frequent itemsets. Our experimental studies on both synthetic and real data show that our proposal achieves high performance for small and large values of k.