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PRL
2006
139views more  PRL 2006»
13 years 7 months ago
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data
This paper presents a partitional dynamic clustering method for interval data based on adaptive Hausdorff distances. Dynamic clustering algorithms are iterative two-step relocatio...
Francisco de A. T. de Carvalho, Renata M. C. R. de...
WCRE
2005
IEEE
14 years 29 days ago
Multiple Layer Clustering of Large Software Systems
Software clustering algorithms presented in the literature rarely incorporate in the clustering process dynamic information, such as the number of function invocations during runt...
Bill Andreopoulos, Aijun An, Vassilios Tzerpos, Xi...
INFSOF
2007
101views more  INFSOF 2007»
13 years 7 months ago
Clustering large software systems at multiple layers
Software clustering algorithms presented in the literature rarely incorporate in the clustering process dynamic information, such as the number of function invocations during runt...
Bill Andreopoulos, Aijun An, Vassilios Tzerpos, Xi...
IDA
1999
Springer
13 years 11 months ago
Discovering Dynamics Using Bayesian Clustering
Abstract. This paper introduces a Bayesian method for clustering dynamic processes and applies it to the characterization of the dynamics of a military scenario. The method models ...
Paola Sebastiani, Marco Ramoni, Paul R. Cohen, Joh...
ML
2002
ACM
246views Machine Learning» more  ML 2002»
13 years 7 months ago
Bayesian Clustering by Dynamics
This paper introduces a Bayesian method for clustering dynamic processes. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to disc...
Marco Ramoni, Paola Sebastiani, Paul R. Cohen