Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
ACM SIGMOD Record
Research issues in data stream association rule mining
ACM SIGMOD Record
Maintenance of maximal frequent itemsets in large databases
Proceedings of the 2007 ACM symposium on Applied computing
Mining itemsets in the presence of missing values
Proceedings of the 2007 ACM symposium on Applied computing
Mining fault-tolerant frequent patterns efficiently with powerful pruning
Proceedings of the 2008 ACM symposium on Applied computing
Anomaly-free incremental output in stream processing
Proceedings of the 17th ACM conference on Information and knowledge management
Frequent spatio-temporal patterns in trajectory data warehouses
Proceedings of the 2009 ACM symposium on Applied Computing
Mining of Frequent Itemsets from Streams of Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining interesting sets and rules in relational databases
Proceedings of the 2010 ACM Symposium on Applied Computing
Mining uncertain data for frequent itemsets that satisfy aggregate constraints
Proceedings of the 2010 ACM Symposium on Applied Computing
A study on interestingness measures for associative classifiers
Proceedings of the 2010 ACM Symposium on Applied Computing
A persistent HY-Tree to efficiently support itemset mining on large datasets
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
Equivalence class transformation based mining of frequent itemsets from uncertain data
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
Frequent pattern mining from time-fading streams of uncertain data
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
A new class of constraints for constrained frequent pattern mining
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Expert Systems with Applications: An International Journal
Stream mining of frequent sets with limited memory
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Discovering frequent itemsets on uncertain data: a systematic review
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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With advances in technology, large amounts of streaming data can be generated continuously by sensors in applications like environment surveillance. Due to the inherited limitation of sensors, these continuous data can be uncertain. This calls for stream mining of uncertain data. In recent years, tree-based algorithms have been proposed to use the sliding window model for mining frequent itemsets from streams of uncertain data. Besides the sliding window model, there are other window models for processing data streams. In this paper, we propose tree-based algorithms that use the damped window model to mine frequent itemsets from streams of uncertain data.