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» Finding Time Series Discords Based on Haar Transform
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ADMA
2006
Springer
112views Data Mining» more  ADMA 2006»
13 years 10 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...
SDM
2007
SIAM
149views Data Mining» more  SDM 2007»
13 years 5 months ago
WAT: Finding Top-K Discords in Time Series Database
Finding discords in time series database is an important problem in a great variety of applications, such as space shuttle telemetry, mechanical industry, biomedicine, and financ...
Yingyi Bu, Oscar Tat-Wing Leung, Ada Wai-Chee Fu, ...
ICDM
2008
IEEE
230views Data Mining» more  ICDM 2008»
13 years 11 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
AMW
2009
13 years 5 months ago
T3: On Mapping Text To Time Series
We investigate if the mapping between text and time series data is feasible such that relevant data mining problems in text can find their counterparts in time series (and vice ver...
Tao Yang, Dongwon Lee
BMCV
2000
Springer
13 years 8 months ago
A New Line Segment Grouping Method for Finding Globally Optimal Line Segments
In this paper we propose a new method for extracting line segments from edge images. Our method basically follows a line segment grouping approach. This approach has many advantage...
Jeong-Hun Jang, Ki-Sang Hong