Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
A fast distributed algorithm for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Mining of Constrained Correlated Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
T-Trees, Vertical Partitioning and Distributed Association Rule Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
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
A high-performance distributed algorithm for mining association rules
Knowledge and Information Systems
Parallel Leap: Large-Scale Maximal Pattern Mining in a Distributed Environment
ICPADS '06 Proceedings of the 12th International Conference on Parallel and Distributed Systems - Volume 1
Finding frequent items in probabilistic data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Mining of Frequent Itemsets from Streams of Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Frequent pattern mining with uncertain data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic frequent itemset mining in uncertain databases
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining uncertain data for frequent itemsets that satisfy aggregate constraints
Proceedings of the 2010 ACM Symposium on Applied Computing
A tree-based approach for frequent pattern mining from uncertain data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Efficient algorithms for the mining of constrained frequent patterns from uncertain data
ACM SIGKDD Explorations Newsletter
Probabilistic spatial queries on existentially uncertain data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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With the advance in technology, sensor networks have been widely used in many application areas such as environmental surveillance. Sensors distributed in these networks serve as good sources for data. This calls for distributed data mining, which searches for implicit, previously unknown, and potentially useful patterns that might be embedded in the distributed data. Many existing distributed data mining algorithms do not allow users to express the patterns to be mined according to their intention via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous patterns that are not interesting to users. Moreover, due to inherited measurement inaccuracies and/or network latencies, the data are often riddled with uncertainty. These call for constrained mining and uncertain data mining. In this paper, we propose a tree-based system for mining frequent sets that satisfy user-defined constraints from a distributed environment such as a wireless sensor network of uncertain data. Experimental results show effectiveness of our proposed system.