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
Anomaly-free incremental output in stream processing
Proceedings of the 17th ACM conference on Information and knowledge management
Mining of Frequent Itemsets from Streams of Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Approximate Frequent Itemset Discovery from Data Stream
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
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
PODS: a new model and processing algorithms for uncertain data streams
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
uCFS2: an enhanced system that mines uncertain data for constrained frequent sets
Proceedings of the Fourteenth International Database Engineering & Applications Symposium
Probabilistic Databases
Frequent pattern mining from time-fading streams of uncertain data
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Mining probabilistic datasets vertically
Proceedings of the 16th International Database Engineering & Applications Sysmposium
EFP-M2: efficient model for mining frequent patterns in transactional database
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
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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Huge volumes of streaming data have been generated by sensors for applications such as environment surveillance. Partially due to the inherited limitation of sensors, these continuous streaming data can be uncertain. Over the past few years, algorithms have been proposed to apply the sliding window or time-fading window model to mine frequent patterns from streams of uncertain data. However, there are also other models to process data streams. In this paper, we propose a landmark-model based system for mining frequent patterns from streams of uncertain data.