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ICDM
2006
IEEE
193views Data Mining» more  ICDM 2006»
14 years 1 months ago
Local Correlation Tracking in Time Series
We address the problem of capturing and tracking local correlations among time evolving time series. Our approach is based on comparing the local auto-covariance matrices (via the...
Spiros Papadimitriou, Jimeng Sun, Philip S. Yu
PCI
2001
Springer
13 years 12 months ago
SEISMO-SURFER: A Prototype for Collecting, Querying, and Mining Seismic Data
Earthquake phenomena constitute a rich source of information over the years. Typically, the frequency of earthquakes worldwide is one every second. Collecting and querying seismic ...
Yannis Theodoridis
ADMA
2006
Springer
112views Data Mining» more  ADMA 2006»
14 years 1 months ago
Finding Time Series Discords Based on Haar Transform
The problem of finding anomaly has received much attention recently. However, most of the anomaly detection algorithms depend on an explicit definition of anomaly, which may be i...
Ada Wai-Chee Fu, Oscar Tat-Wing Leung, Eamonn J. K...
ICDM
2008
IEEE
230views Data Mining» more  ICDM 2008»
14 years 2 months ago
Clustering Distributed Time Series in Sensor Networks
Event detection is a critical task in sensor networks, especially for environmental monitoring applications. Traditional solutions to event detection are based on analyzing one-sh...
Jie Yin, Mohamed Medhat Gaber
SDM
2003
SIAM
184views Data Mining» more  SDM 2003»
13 years 9 months ago
Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
The problem of finding clusters in data is challenging when clusters are of widely differing sizes, densities and shapes, and when the data contains large amounts of noise and out...
Levent Ertöz, Michael Steinbach, Vipin Kumar