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INFORMATICALT
2008
124views more  INFORMATICALT 2008»
13 years 8 months ago
Hierarchical Adaptive Clustering
This paper studies an adaptive clustering problem. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We prop...
Gabriela Serban, Alina Campan
PRL
2006
139views more  PRL 2006»
13 years 8 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...
CONCURRENCY
2010
75views more  CONCURRENCY 2010»
13 years 9 months ago
An Open Source performance tools software suite for scientific computing
With the rapid replacement of closed, homogeneous, proprietary HPC systems by heterogeneous, Linux-MPI cluster systems, the state of performance monitoring and analysis tools has ...
Philip J. Mucci, Tushar Mohan
COMPGEOM
2006
ACM
14 years 2 months ago
How slow is the k-means method?
The k-means method is an old but popular clustering algorithm known for its observed speed and its simplicity. Until recently, however, no meaningful theoretical bounds were known...
David Arthur, Sergei Vassilvitskii
ICPR
2008
IEEE
14 years 10 months ago
Growing neural gas for temporal clustering
Conventional clustering techniques provide a static snapshot of each vector's commitment to every group. With additive datasets, however, existing methods may not be sufficie...
Isaac J. Sledge, James M. Keller