Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Exploiting succinct constraints using FP-trees
ACM SIGKDD Explorations Newsletter
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
DualMiner: a dual-pruning algorithm for itemsets with constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient dynamic mining of constrained frequent sets
ACM Transactions on Database Systems (TODS)
Pushing Convertible Constraints in Frequent Itemset Mining
Data Mining and Knowledge Discovery
Extending the state-of-the-art of constraint-based pattern discovery
Data & Knowledge Engineering
Mining uncertain data for frequent itemsets that satisfy aggregate constraints
Proceedings of the 2010 ACM Symposium on Applied Computing
Frequent itemset mining of uncertain data streams using the damped window model
Proceedings of the 2011 ACM Symposium on Applied Computing
Equivalence class transformation based mining of frequent itemsets from uncertain data
Proceedings of the 2011 ACM Symposium on Applied Computing
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 constrained frequent pattern mining system for handling aggregate constraints
Proceedings of the 16th International Database Engineering & Applications Sysmposium
Mining popular patterns from transactional databases
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Stream mining of frequent sets with limited memory
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Pushing constraints into data streams
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
| Hi-index | 0.00 |
Most of the frequent pattern mining algorithms search for all frequent patterns. However, there are many real-life situations in which users are interested in only some tiny portions of the mined frequent patterns. For mining of constrained frequent patterns, several classes of user constraints---such as anti-monotone constraints---have been proposed and their properties have been exploited. In this paper, we introduce a new class of constraints called mixed monotone constraints. We exploit its property for effective mining of frequent patterns satisfying user constraints that sum both positive and negative numerical values.