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» Evaluating algorithms that learn from data streams
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DATAMINE
2006
230views more  DATAMINE 2006»
14 years 10 months ago
Mining top-K frequent itemsets from data streams
Frequent pattern mining on data streams is of interest recently. However, it is not easy for users to determine a proper frequency threshold. It is more reasonable to ask users to ...
Raymond Chi-Wing Wong, Ada Wai-Chee Fu
SIGMOD
2009
ACM
175views Database» more  SIGMOD 2009»
15 years 10 months ago
Keyword search on structured and semi-structured data
Empowering users to access databases using simple keywords can relieve the users from the steep learning curve of mastering a structured query language and understanding complex a...
Yi Chen, Wei Wang 0011, Ziyang Liu, Xuemin Lin
PODS
2002
ACM
136views Database» more  PODS 2002»
15 years 10 months ago
Models and Issues in Data Stream Systems
In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations...
Brian Babcock, Shivnath Babu, Mayur Datar, Rajeev ...
ICASSP
2010
IEEE
14 years 8 months ago
Learning from other subjects helps reducing Brain-Computer Interface calibration time
A major limitation of Brain-Computer Interfaces (BCI) is their long calibration time, as much data from the user must be collected in order to tune the BCI for this target user. I...
Fabien Lotte, Cuntai Guan
90
Voted
JIIS
2006
147views more  JIIS 2006»
14 years 10 months ago
Mining sequential patterns from data streams: a centroid approach
In recent years, emerging applications introduced new constraints for data mining methods. These constraints are typical of a new kind of data: the data streams. In data stream pro...
Alice Marascu, Florent Masseglia