Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window
Knowledge and Information Systems
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Maintaining frequent closed itemsets over a sliding window
Journal of Intelligent Information Systems
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Verifying and Mining Frequent Patterns from Large Windows over Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
A Fast Algorithm for Mining Rare Itemsets
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
RP-Tree: rare pattern tree mining
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Finding sporadic rules using apriori-inverse
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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There has been some research in the area of rare pattern mining where the researchers try to capture patterns involving events that are unusual in a dataset. These patterns are considered more useful than frequent patterns in some domain, including detection of computer attacks, or fraudulent credit transactions. Until now, most of the research in this area concentrates only on finding rare rules in a static dataset. There is a proliferation of applications which generate data streams, such as network logs and banking transactions. Applying techniques for static datasets is not practical for data streams. In this paper we propose a novel approach called Streaming Rare Pattern Tree (SRP-Tree), which finds rare rules in a data stream environment using a sliding window, and show that it is faster than current approaches.