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» Bayesian multiscale analysis for time series data
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KDD
2007
ACM
209views Data Mining» more  KDD 2007»
14 years 7 months ago
Temporal causal modeling with graphical granger methods
The need for mining causality, beyond mere statistical correlations, for real world problems has been recognized widely. Many of these applications naturally involve temporal data...
Andrew Arnold, Yan Liu, Naoki Abe
COMPGEOM
2011
ACM
12 years 11 months ago
Persistence-based clustering in riemannian manifolds
We present a clustering scheme that combines a mode-seeking phase with a cluster merging phase in the corresponding density map. While mode detection is done by a standard graph-b...
Frédéric Chazal, Leonidas J. Guibas,...
SDM
2009
SIAM
170views Data Mining» more  SDM 2009»
14 years 4 months ago
Mining Complex Spatio-Temporal Sequence Patterns.
Mining sequential movement patterns describing group behaviour in potentially streaming spatio-temporal data sets is a challenging problem. Movements are typically noisy and often...
Florian Verhein
KDD
2008
ACM
186views Data Mining» more  KDD 2008»
14 years 7 months ago
Cut-and-stitch: efficient parallel learning of linear dynamical systems on smps
Multi-core processors with ever increasing number of cores per chip are becoming prevalent in modern parallel computing. Our goal is to make use of the multi-core as well as multi...
Lei Li, Wenjie Fu, Fan Guo, Todd C. Mowry, Christo...
ESANN
2006
13 years 8 months ago
Random Forests Feature Selection with K-PLS: Detecting Ischemia from Magnetocardiograms
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...
Long Han, Mark J. Embrechts, Boleslaw K. Szymanski...