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» A New Algorithm for Mining Sequential Patterns
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KDD
1998
ACM
118views Data Mining» more  KDD 1998»
13 years 12 months ago
A Belief-Driven Method for Discovering Unexpected Patterns
Several pattern discovery methods proposed in the data mining literature have the drawbacks that they discover too many obvious or irrelevant patterns and that they do not leverag...
Balaji Padmanabhan, Alexander Tuzhilin
KDD
2006
ACM
163views Data Mining» more  KDD 2006»
14 years 8 months ago
New EM derived from Kullback-Leibler divergence
We introduce a new EM framework in which it is possible not only to optimize the model parameters but also the number of model components. A key feature of our approach is that we...
Longin Jan Latecki, Marc Sobel, Rolf Lakämper
PKDD
2009
Springer
129views Data Mining» more  PKDD 2009»
14 years 2 months ago
Considering Unseen States as Impossible in Factored Reinforcement Learning
Abstract. The Factored Markov Decision Process (FMDP) framework is a standard representation for sequential decision problems under uncertainty where the state is represented as a ...
Olga Kozlova, Olivier Sigaud, Pierre-Henri Wuillem...
ICDE
2010
IEEE
290views Database» more  ICDE 2010»
13 years 11 months ago
The Model-Summary Problem and a Solution for Trees
Modern science is collecting massive amounts of data from sensors, instruments, and through computer simulation. It is widely believed that analysis of this data will hold the key ...
Biswanath Panda, Mirek Riedewald, Daniel Fink
KDD
1998
ACM
160views Data Mining» more  KDD 1998»
13 years 12 months ago
Algorithms for Characterization and Trend Detection in Spatial Databases
1 The number and the size of spatial databases, e.g. for geomarketing, traffic control or environmental studies, are rapidly growing which results in an increasing need for spatial...
Martin Ester, Alexander Frommelt, Hans-Peter Krieg...