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» Temporal granular logic for temporal data mining
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KDD
2010
ACM
247views Data Mining» more  KDD 2010»
15 years 3 months ago
Metric forensics: a multi-level approach for mining volatile graphs
Advances in data collection and storage capacity have made it increasingly possible to collect highly volatile graph data for analysis. Existing graph analysis techniques are not ...
Keith Henderson, Tina Eliassi-Rad, Christos Falout...
132
Voted
ECCV
2008
Springer
16 years 3 months ago
Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners
Abstract. The use of sparse invariant features to recognise classes of actions or objects has become common in the literature. However, features are often "engineered" to...
Andrew Gilbert, John Illingworth, Richard Bowden
SDM
2008
SIAM
130views Data Mining» more  SDM 2008»
15 years 3 months ago
Mining Sequence Classifiers for Early Prediction
Supervised learning on sequence data, also known as sequence classification, has been well recognized as an important data mining task with many significant applications. Since te...
Zhengzheng Xing, Jian Pei, Guozhu Dong, Philip S. ...
PVLDB
2008
107views more  PVLDB 2008»
15 years 1 months ago
Finding relevant patterns in bursty sequences
Sequence data is ubiquitous and finding frequent sequences in a large database is one of the most common problems when analyzing sequence data. Unfortunately many sources of seque...
Alexander Lachmann, Mirek Riedewald
118
Voted
ROBOCUP
1999
Springer
95views Robotics» more  ROBOCUP 1999»
15 years 6 months ago
Spatial Agents Implemented in a Logical Expressible Language
In this paper, we present a multi-layered architecture for spatial and temporal agents. The focus is laid on the declarativity of the approach, which makes agent scripts expressive...
Frieder Stolzenburg, Oliver Obst, Jan Murray, Bj&o...