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» Mining data streams: a review
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KDD
2010
ACM
247views Data Mining» more  KDD 2010»
13 years 10 months ago
Metric forensics: a multi-level approach for mining volatile graphs
Advances in data collection and storage capacity have made it increasingly possible to collect highly volatile graph data for analysis. Existing graph analysis techniques are not ...
Keith Henderson, Tina Eliassi-Rad, Christos Falout...
ICDM
2006
IEEE
124views Data Mining» more  ICDM 2006»
14 years 2 months ago
Finding "Who Is Talking to Whom" in VoIP Networks via Progressive Stream Clustering
Technologies that use the Internet network to deliver voice communications have the potential to reduce costs and improve access to communications services around the world. Howev...
Olivier Verscheure, Michail Vlachos, Aris Anagnost...
BIB
2008
109views more  BIB 2008»
13 years 9 months ago
Computational intelligence approaches for pattern discovery in biological systems
Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) ...
Gary B. Fogel
COMAD
2009
13 years 10 months ago
Categorizing Concepts for Detecting Drifts in Stream
Mining evolving data streams for concept drifts has gained importance in applications like customer behavior analysis, network intrusion detection, credit card fraud detection. Se...
Sharanjit Kaur, Vasudha Bhatnagar, Sameep Mehta, S...
JIIS
2008
133views more  JIIS 2008»
13 years 8 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