Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 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
Constrained frequent pattern mining: a pattern-growth view
ACM SIGKDD Explorations Newsletter
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Pincer-Search: An Efficient Algorithm for Discovering the Maximum Frequent Set
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Frequent Closures as a Concise Representation for Binary Data Mining
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints
Data Mining and Knowledge Discovery
ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints
ICDM '03 Proceedings of the Third 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
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
On Closed Constrained Frequent Pattern Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Generating a Condensed Representation for Association Rules
Journal of Intelligent Information Systems
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
BitTableFI: An efficient mining frequent itemsets algorithm
Knowledge-Based Systems
Optimization of association rule mining queries
Intelligent Data Analysis
Index-BitTableFI: An improved algorithm for mining frequent itemsets
Knowledge-Based Systems
Efficient Algorithms for Mining Frequent Itemsets with Constraint
KSE '11 Proceedings of the 2011 Third International Conference on Knowledge and Systems Engineering
DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets
Expert Systems with Applications: An International Journal
Mining frequent itemsets with dualistic constraints
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
A lattice-based approach for mining most generalization association rules
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
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Constraint-based frequent itemset mining is necessary when the needs and interests of users are the top priority. In this task, two opposite types of constraint are studied, namely anti-monotone and monotone constraints. Previous approaches have mainly mined frequent itemsets that satisfy one of these two types of constraint. Mining frequent itemsets that satisfy both types is of interest. The present study considers the problem of mining frequent itemsets with the following two conditions: they include a set C"0 (monotone) and contain no items of set C'"1 (anti-monotone), where the intersection of C"0 and C'"1 is empty and they are changed regularly. A unique representation of frequent itemsets restricted on C"0 and C'"1 using closed itemsets and their generators is proposed. Then, an algorithm called MFS_DoubleCons is developed to quickly and distinctly generate all frequent itemsets that satisfy the constraints from the lattice of closed itemsets and generators instead of mining them directly from the database. The theoretical results are proven to be reliable. Extensive experiments on a broad range of synthetic and real databases that compare MFS_DoubleCons to dEclat-DC (a modified version of dEclat utilized to mine frequent itemsets with constraints) show the effectiveness of our approach.