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» Visual Mining of Spatial Time Series Data
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WWW
2007
ACM
14 years 8 months ago
Why we search: visualizing and predicting user behavior
The aggregation and comparison of behavioral patterns on the WWW represent a tremendous opportunity for understanding past behaviors and predicting future behaviors. In this paper...
Eytan Adar, Daniel S. Weld, Brian N. Bershad, Stev...
KDD
2003
ACM
118views Data Mining» more  KDD 2003»
14 years 8 months ago
Generating English summaries of time series data using the Gricean maxims
We are developing technology for generating English textual summaries of time-series data, in three domains: weather forecasts, gas-turbine sensor readings, and hospital intensive...
Somayajulu Sripada, Ehud Reiter, Jim Hunter, Jin Y...
SSD
2009
Springer
124views Database» more  SSD 2009»
14 years 2 months ago
ELKI in Time: ELKI 0.2 for the Performance Evaluation of Distance Measures for Time Series
ELKI is a unied software framework, designed as a tool suitable for evaluation of dierent algorithms on high dimensional realvalued feature-vectors. A special case of high dimens...
Elke Achtert, Thomas Bernecker, Hans-Peter Kriegel...
KDD
1997
ACM
213views Data Mining» more  KDD 1997»
13 years 11 months ago
A Probabilistic Approach to Fast Pattern Matching in Time Series Databases
Theproblemof efficiently and accurately locating patterns of interest in massivetimeseries data sets is an important and non-trivial problemin a wide variety of applications, incl...
Eamonn J. Keogh, Padhraic Smyth
SDM
2009
SIAM
291views Data Mining» more  SDM 2009»
14 years 4 months ago
Detection and Characterization of Anomalies in Multivariate Time Series.
Anomaly detection in multivariate time series is an important data mining task with applications to ecosystem modeling, network traffic monitoring, medical diagnosis, and other d...
Christopher Potter, Haibin Cheng, Pang-Ning Tan, S...