Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Online algorithms for mining inter-stream associations from large sensor networks
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
SNIF TOOL: sniffing for patterns in continuous streams
Proceedings of the 17th ACM conference on Information and knowledge management
Flexible least squares for temporal data mining and statistical arbitrage
Expert Systems with Applications: An International Journal
Incremental pattern discovery on streams, graphs and tensors
ACM SIGKDD Explorations Newsletter
Online pairing of VoIP conversations
The VLDB Journal — The International Journal on Very Large Data Bases
CLAP: Collaborative pattern mining for distributed information systems
Decision Support Systems
RainMon: an integrated approach to mining bursty timeseries monitoring data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams
International Journal of Data Warehousing and Mining
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Given m groups of streams which consist of n1,...,nm co-evolving streams in each group, we want to: (i) incrementally find local patterns within a single group, (ii) efficiently obtain global patterns across groups, and more importantly, (iii) efficiently do that in real time while limiting shared information across groups. In this paper, we present a distributed, hierarchical algorithm addressing these problems. Our experimental case study confirms that the proposed method can perform hierarchical correlation detection efficiently and effectively.