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» Maintaining variance and k-medians over data stream windows
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PODS
2003
ACM
143views Database» more  PODS 2003»
14 years 3 months ago
Maintaining variance and k-medians over data stream windows
The sliding window model is useful for discounting stale data in data stream applications. In this model, data elements arrive continually and only the most recent N elements are ...
Brian Babcock, Mayur Datar, Rajeev Motwani, Liadan...
KAIS
2006
110views more  KAIS 2006»
13 years 3 months ago
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window
Yun Chi, Haixun Wang, Philip S. Yu, Richard R. Mun...
SIAMCOMP
2002
152views more  SIAMCOMP 2002»
13 years 3 months ago
Maintaining Stream Statistics over Sliding Windows
We consider the problem of maintaining aggregates and statistics over data streams, with respect to the last N data elements seen so far. We refer to this model as the sliding wind...
Mayur Datar, Aristides Gionis, Piotr Indyk, Rajeev...
JIIS
2008
133views more  JIIS 2008»
13 years 3 months ago
Maintaining frequent closed itemsets over a sliding window
In this paper, we study the incremental update of Frequent Closed Itemsets (FCIs) over a sliding window in a high-speed data stream. We propose the notion of semi-FCIs, which is to...
James Cheng, Yiping Ke, Wilfred Ng
ICDM
2006
IEEE
227views Data Mining» more  ICDM 2006»
13 years 9 months ago
Incremental Mining of Sequential Patterns over a Stream Sliding Window
Incremental mining of sequential patterns from data streams is one of the most challenging problems in mining data streams. However, previous work of mining sequential patterns fr...
Chin-Chuan Ho, Hua-Fu Li, Fang-Fei Kuo, Suh-Yin Le...