Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 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
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
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
ExMiner: an efficient algorithm for mining top-k frequent patterns
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Discovering Periodic-Frequent Patterns in Transactional Databases
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and 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
Discovering diverse-frequent patterns in transactional databases
Proceedings of the 17th International Conference on Management of Data
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Conventional frequent pattern mining algorithms require users to specify some minimum support threshold, which is not easy to identify without knowledge about the datasets in advance. This difficulty leads users to dilemma that either they may lose useful information or may not be able to screen for the interesting knowledge from huge presented frequent patterns sets. Mining top-k frequent patterns allows users to control the number of patterns to be discovered for analyzing. In this paper, we propose an optimized version of the ExMiner, called OExMiner, to mine the top-k frequent patterns from a large scale dataset efficiently and effectively. In order to improve the user-friendliness and also the performance of the system we proposed other 2 methods, extended from OExMiner, called Seq-Miner and Seq-BOMA to mine top-k frequent patterns sequentially. Experiments on both synthetic and real data show that our proposed methods are much more efficient and effective compared to the existing ones.