Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sketching probabilistic data streams
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A Framework for Clustering Uncertain Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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The analysis and management of uncertain data has attracted a lot of attention recently in many important applications such as pattern recognition and sensor network. Frequent itemset mining is often useful in analyzing uncertain data in those applications. However, previous works just focus on the static uncertain data instead of uncertain streams. In this paper, we study the problem of mining top-k FIs in uncertain streams. We propose an efficient algorithm, called UTK-FI, based on sliding-window and Chernoff bound techniques for finding k most frequent itemsets of different sizes. Experimental results show that our algorithm performs much better than many established methods in uncertain streams environment.