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
Efficient mining of association rules using closed itemset lattices
Information Systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A condensed representation to find frequent patterns
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th 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
Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive and Resource-Aware Mining of Frequent Sets
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Advances in frequent itemset mining implementations: report on FIMI'03
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Frequent closed itemset based algorithms: a thorough structural and analytical survey
ACM SIGKDD Explorations Newsletter
CFI-Stream: mining closed frequent itemsets in data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of understandable patterns from multivariate interval time series
Data Mining and Knowledge Discovery
A Galois Lattice framework to handle updates in the mining of closed itemsets in dynamic databases
COMPUTE '08 Proceedings of the 1st Bangalore Annual Compute Conference
Fast mining maximal sequential patterns
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
An efficient algorithm for mining closed inter-transaction itemsets
Data & Knowledge Engineering
Fast mining of closed sequential patterns
WSEAS Transactions on Computers
A framework for mining top-k frequent closed itemsets using order preserving generators
Proceedings of the 2nd Bangalore Annual Compute Conference
Mining non-derivable frequent itemsets over data stream
Data & Knowledge Engineering
Finding Frequent Closed Itemsets in Sliding Window in Linear Time
IEICE - Transactions on Information and Systems
Data & Knowledge Engineering
Frequent closed multi-dimensional multi-level pattern mining
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
Using a reinforced concept lattice to incrementally mine association rules from closed itemsets
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
GC-tree: a fast online algorithm for mining frequent closed itemsets
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
TGC-tree: an online algorithm tracing closed itemset and transaction set simultaneously
LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
FeedRank: a semantic-based management system of web feeds
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Margin-closed frequent sequential pattern mining
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
Mining relaxed closed subspace clusters
Proceedings of the 48th Annual Southeast Regional Conference
Mining minimal non-redundant association rules using frequent itemsets lattice
International Journal of Intelligent Systems Technologies and Applications
DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets
Expert Systems with Applications: An International Journal
Mining top-k association rules
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Mining top-K non-redundant association rules
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Interrelation analysis of celestial spectra data using constrained frequent pattern trees
Knowledge-Based Systems
A lattice-based approach for mining most generalization association rules
Knowledge-Based Systems
MEI: An efficient algorithm for mining erasable itemsets
Engineering Applications of Artificial Intelligence
Efficient frequent itemset mining methods over time-sensitive streams
Knowledge-Based Systems
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This paper presents a new scalable algorithm for discovering closed frequent itemsets, a lossless and condensed representation of all the frequent itemsets that can be mined from a transactional database. Our algorithm exploits a divide-and-conquer approach and a bitwise vertical representation of the database and adopts a particular visit and partitioning strategy of the search space based on an original theoretical framework, which formalizes the problem of closed itemsets mining in detail. The algorithm adopts several optimizations aimed to save both space and time in computing itemset closures and their supports. In particular, since one of the main problems in this type of algorithms is the multiple generation of the same closed itemset, we propose a new effective and memory-efficient pruning technique, which, unlike other previous proposals, does not require the whole set of closed patterns mined so far to be kept in the main memory. This technique also permits each visited partition of the search space to be mined independently in any order and, thus, also in parallel. The tests conducted on many publicly available data sets show that our algorithm is scalable and outperforms other state-of-the-art algorithms like Closet+ and FP-Close, in some cases by more than one order of magnitude. More importantly, the performance improvements become more and more significant as the support threshold is decreased.