Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 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
Automatic subspace clustering of high dimensional data for data mining applications
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
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Constrained frequent pattern mining: a pattern-growth view
ACM SIGKDD Explorations Newsletter
Exploiting succinct constraints using FP-trees
ACM SIGKDD Explorations Newsletter
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
Scalable Techniques for Mining Causal Structures
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Mining Frequent Itemsets Using Support Constraints
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Efficient Mining of Constrained Correlated Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Mining frequent itemsets with partial enumeration
Proceedings of the 44th annual Southeast regional conference
Searching for high-support itemsets in itemset trees
Intelligent Data Analysis
Mining top-k strongly correlated item pairs without minimum correlation threshold
International Journal of Knowledge-based and Intelligent Engineering Systems
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
Mining N-most interesting itemsets without support threshold by the COFI-tree
International Journal of Business Intelligence and Data Mining
Fast detection of database system abuse behaviors based on data mining approach
Proceedings of the 2nd international conference on Scalable information systems
A behavioral pattern adapted to individual for providing ubiquitous service in intelligent space
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Mining top-k frequent patterns in the presence of the memory constraint
The VLDB Journal — The International Journal on Very Large Data Bases
The VLDB Journal — The International Journal on Very Large Data Bases
Short communication: TOPSIS: Finding Top-K significant N-itemsets in sliding windows adaptively
Knowledge-Based Systems
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Expert Systems with Applications: An International Journal
A sliding window method for finding top-k path traversal patterns over streaming Web click-sequences
Expert Systems with Applications: An International Journal
Efficient discovery of risk patterns in medical data
Artificial Intelligence in Medicine
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
Finding N-Most Prevalent Colocated Event Sets
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Efficient incremental mining of top-K frequent closed itemsets
DS'07 Proceedings of the 10th international conference on Discovery science
Secure transaction protocol analysis: models and applications
Secure transaction protocol analysis: models and applications
TOPSIL-Miner: an efficient algorithm for mining top-K significant itemsets over data streams
Knowledge and Information Systems
Using ontologies to facilitate post-processing of association rules by domain experts
Information Sciences: an International Journal
Mining top-k regular-frequent itemsets using database partitioning and support estimation
Expert Systems with Applications: An International Journal
On exploring the power-law relationship in the itemset support distribution
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Mining spatial colocation patterns: a different framework
Data Mining and Knowledge Discovery
Mining top-K high utility itemsets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Min-Max itemset trees for dense and categorical datasets
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Mining high coherent association rules with consideration of support measure
Expert Systems with Applications: An International Journal
Mining top-k frequent patterns over data streams sliding window
Journal of Intelligent Information Systems
Scaling up cosine interesting pattern discovery: A depth-first method
Information Sciences: an International Journal
Efficient mining of maximal correlated weight frequent patterns
Intelligent Data Analysis
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In classical association rules mining, a minimum support threshold is assumed to be available for mining frequent itemsets. However, setting such a threshold is typically hard. In this paper, we handle a more practical problem; roughly speaking, it is to mine N k-itemsets with the highest supports for k up to a certain k_{max} value. We call the results the N-most interesting itemsets. Generally, it is more straightforward for users to determine N and k_{max}. We propose two new algorithms, LOOPBACK and BOMO. Experiments show that our methods outperform the previously proposed Itemset-Loop algorithm, and the performance of BOMO can be an order of magnitude better than the original FP-tree algorithm, even with the assumption of an optimally chosen support threshold. We also propose the mining of "N-most interesting k-itemsets with item constraints.驴 This allows user to specify different degrees of interestingness for different itemsets. Experiments show that our proposed Double FP-trees algorithm, which is based on BOMO, is highly efficient in solving this problem.