Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
Efficient frequent pattern mining over data streams
Proceedings of the 17th ACM conference on Information and knowledge management
SPO-Tree: efficient single pass ordered incremental pattern mining
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
Extrapolation prefix tree for data stream mining using a landmark model
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
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Although frequent pattern mining techniques have been extensively studied, the extension of their application onto data streams has been challenging. Due to data streams being continuous and unbounded, an efficient algorithm that avoids multiple scans of data is needed. In this paper we propose Kernel-Tree (KerTree), a single pass tree structured technique that mines frequent patterns in a data stream based on forecasting the support of current items in the future state. Unlike previous techniques that build a tree based on the support of items in the previous block, KerTree performs an estimation of item support in the next block and builds the tree based on the estimation. By building the tree on an estimated future state, KerTree effectively reduces the need to restructure for every block and thus results in a better performance and mines the complete set of frequent patterns from the stream while maintaining a compact structure.