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» Methods for finding frequent items in data streams
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HOTOS
2007
IEEE
13 years 11 months ago
Hyperspaces for Object Clustering and Approximate Matching in Peer-to-Peer Overlays
Existing distributed hash tables provide efficient mechanisms for storing and retrieving a data item based on an exact key, but are unsuitable when the search key is similar, but ...
Bernard Wong, Ymir Vigfusson, Emin Gün Sirer
IS
2008
13 years 7 months ago
Continuous subspace clustering in streaming time series
Performing data mining tasks in streaming data is considered a challenging research direction, due to the continuous data evolution. In this work, we focus on the problem of clust...
Maria Kontaki, Apostolos N. Papadopoulos, Yannis M...
DATAMINE
2008
219views more  DATAMINE 2008»
13 years 7 months ago
Correlating burst events on streaming stock market data
Abstract We address the problem of monitoring and identification of correlated burst patterns in multi-stream time series databases. We follow a two-step methodology: first we iden...
Michail Vlachos, Kun-Lung Wu, Shyh-Kwei Chen, Phil...
KDD
2004
ACM
124views Data Mining» more  KDD 2004»
14 years 8 months ago
Support envelopes: a technique for exploring the structure of association patterns
This paper introduces support envelopes--a new tool for analyzing association patterns--and illustrates some of their properties, applications, and possible extensions. Specifical...
Michael Steinbach, Pang-Ning Tan, Vipin Kumar
DASFAA
2005
IEEE
157views Database» more  DASFAA 2005»
14 years 1 months ago
Adaptively Detecting Aggregation Bursts in Data Streams
Finding bursts in data streams is attracting much attention in research community due to its broad applications. Existing burst detection methods suffer the problems that 1) the p...
Aoying Zhou, Shouke Qin, Weining Qian