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Frequent itemset mining of uncertain data streams using the damped window model
2011
Proceedings of the 2011 ACM Symposium on Applied Computing - SAC '11
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
doi:10.1145/1982185.1982393
dblp:conf/sac/LeungJ11
fatcat:hewtqt4w5zeoffnero55mujlo4