estWin: adaptively monitoring the recent change of frequent itemsets over online data streams
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A survey on algorithms for mining frequent itemsets over data streams
Knowledge and Information Systems
DELAY: A Lazy Approach for Mining Frequent Patterns over High Speed Data Streams
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Efficient Approximate Mining of Frequent Patterns over Transactional Data Streams
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Data Mining and Knowledge Discovery
Which Is Better for Frequent Pattern Mining: Approximate Counting or Sampling?
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Mining informative rule set for prediction over a sliding window
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Discovery of frequent patterns in transactional data streams
Transactions on large-scale data- and knowledge-centered systems II
Discovery of frequent patterns in transactional data streams
Transactions on large-scale data- and knowledge-centered systems II
Mining frequent itemsets over distributed data streams by continuously maintaining a global synopsis
Data Mining and Knowledge Discovery
Search method of time sensitive frequent itemsets in data streams
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Using a real-time top-k algorithm to mine the most frequent items over multiple streams
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
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We propose a false-negative approach to approximate the set of frequent itemsets (FIs) over a sliding window. Existing approximate algorithms use an error parameter, ε, to control the accuracy of the mining result. However, the use of ε leads to a dilemma. A smaller ε gives a more accurate mining result but higher computational complexity, while increasing ε degrades the mining accuracy. We address this dilemma by introducing a progressively increasing minimum support function. When an itemset is retained in the window longer, we require its minimum support to approach the minimum support of an FI. Thus, the number of potential FIs to be maintained is greatly reduced. Our experiments show that our algorithm not only attains highly accurate mining results, but also runs significantly faster and consumes less memory than do existing algorithms for mining FIs over a sliding window.