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» Online Data Mining for Co-Evolving Time Sequences
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GVD
2004
107views Database» more  GVD 2004»
13 years 6 months ago
A Representation of Time Series for Temporal Web Mining
Data with temporal information is constantly generated, sampled, gathered, and analyzed in different domains, such as medicine, finance, engineering, environmental sciences, and e...
Mireille Samia
SDM
2007
SIAM
198views Data Mining» more  SDM 2007»
13 years 6 months ago
Learning from Time-Changing Data with Adaptive Windowing
We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, inst...
Albert Bifet, Ricard Gavaldà
ICDE
2008
IEEE
214views Database» more  ICDE 2008»
14 years 6 months ago
Efficient Online Subsequence Searching in Data Streams under Dynamic Time Warping Distance
Abstract- Data streams of real numbers are generated naturally in many applications. The technology of online subsequence searching in data streams becomes more and more important ...
Mi Zhou, Man Hon Wong
KDD
2003
ACM
194views Data Mining» more  KDD 2003»
14 years 5 months ago
Finding recent frequent itemsets adaptively over online data streams
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is more likely to be c...
Joong Hyuk Chang, Won Suk Lee
MLDM
2009
Springer
13 years 11 months ago
Relational Frequent Patterns Mining for Novelty Detection from Data Streams
We face the problem of novelty detection from stream data, that is, the identification of new or unknown situations in an ordered sequence of objects which arrive on-line, at cons...
Michelangelo Ceci, Annalisa Appice, Corrado Loglis...