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
Efficient closed pattern mining in the presence of tough block constraints
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Optimizing Constraint-Based Mining by Automatically Relaxing Constraints
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Frequency-based views to pattern collections
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
Extending the state-of-the-art of constraint-based pattern discovery
Data & Knowledge Engineering
Constraint-based sequential pattern mining: the pattern-growth methods
Journal of Intelligent Information Systems
Frequency-based views to pattern collections
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
Mining association rules with multi-dimensional constraints
Journal of Systems and Software
Pushing tougher constraints in frequent pattern mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
An efficient framework for mining flexible constraints
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
On approximation algorithms for data mining applications
Efficient Approximation and Online Algorithms
Exploiting virtual patterns for automatically pruning the search space
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Discriminatory confidence analysis in pattern mining
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
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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. In this paper 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.