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» CORE: Nonparametric Clustering of Large Numeric Databases.
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SDM
2009
SIAM
144views Data Mining» more  SDM 2009»
14 years 1 months ago
CORE: Nonparametric Clustering of Large Numeric Databases.
Current clustering techniques are able to identify arbitrarily shaped clusters in the presence of noise, but depend on carefully chosen model parameters. The choice of model param...
Andrej Taliun, Arturas Mazeika, Michael H. Bö...
KDD
1998
ACM
123views Data Mining» more  KDD 1998»
13 years 8 months ago
Scaling Clustering Algorithms to Large Databases
Practical clustering algorithms require multiple data scans to achieve convergence. For large databases, these scans become prohibitively expensive. We present a scalable clusteri...
Paul S. Bradley, Usama M. Fayyad, Cory Reina
CORR
2010
Springer
145views Education» more  CORR 2010»
13 years 4 months ago
Feature Level Clustering of Large Biometric Database
This paper proposes an efficient technique for partitioning large biometric database during identification. In this technique feature vector which comprises of global and local de...
Hunny Mehrotra, Dakshina Ranjan Kisku, V. Bhawani ...
CLUSTER
2002
IEEE
13 years 9 months ago
Leveraging Standard Core Technologies to Programmatically Build Linux Cluster Appliances
Clusters have made the jump from lab prototypes to fullfledged production computing platforms. The number, variety, and specialized configurations of these machines are increasi...
Mason J. Katz, Philip M. Papadopoulos, Greg Bruno
PAMI
2010
164views more  PAMI 2010»
13 years 2 months ago
Large-Scale Discovery of Spatially Related Images
— We propose a randomized data mining method that finds clusters of spatially overlapping images. The core of the method relies on the min-Hash algorithm for fast detection of p...
Ondrej Chum, Jiri Matas