Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Data mining, hypergraph transversals, and machine learning (extended abstract)
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimization of constrained frequent set queries with 2-variable constraints
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Exploratory mining via constrained frequent set queries
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth 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
ACM SIGKDD Explorations Newsletter
Discovery in multi-attribute data with user-defined constraints
ACM SIGKDD Explorations Newsletter
Mining Frequent Item Sets with Convertible Constraints
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
Mining Free Itemsets under Constraints
IDEAS '01 Proceedings of the International Database Engineering & Applications Symposium
DualMiner: a dual-pruning algorithm for itemsets with constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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The subfield of itemset mining is essentially a collection of algorithms. Whenever a new type of constraint is discovered, a specialized algorithm is proposed to handle it. All of these algorithms are highly tuned to take advantage of the unique properties of their associated constraints, and so they are not very compatible with other constraints. We present a more unified view of mining constrained itemsets such that most existing algorithms can be easily extended to handle constraints for which they were not designed a-priori. We apply this technique to mining itemsets with restrictions on their variance — a problem that has been open for several years in the data mining community.