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» Clustering Large Datasets in Arbitrary Metric Spaces
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CGF
2010
189views more  CGF 2010»
13 years 7 months ago
Space-in-Time and Time-in-Space Self-Organizing Maps for Exploring Spatiotemporal Patterns
Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data canno...
Gennady L. Andrienko, Natalia V. Andrienko, Sebast...
ESANN
2006
13 years 8 months ago
An algorithm for fast and reliable ESOM learning
The training of Emergent Self-organizing Maps (ESOM ) with large datasets can be a computationally demanding task. Batch learning may be used to speed up training. It is demonstrat...
Mario Nöcker, Fabian Mörchen, Alfred Ult...
BMCBI
2005
112views more  BMCBI 2005»
13 years 7 months ago
Visualization methods for statistical analysis of microarray clusters
Background: The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determin...
Matthew A. Hibbs, Nathaniel C. Dirksen, Kai Li, Ol...
ICDE
2012
IEEE
343views Database» more  ICDE 2012»
11 years 9 months ago
Bi-level Locality Sensitive Hashing for k-Nearest Neighbor Computation
We present a new Bi-level LSH algorithm to perform approximate k-nearest neighbor search in high dimensional spaces. Our formulation is based on a two-level scheme. In the first ...
Jia Pan, Dinesh Manocha
CVPR
2008
IEEE
14 years 9 months ago
Graph commute times for image representation
We introduce a new image representation that encompasses both the general layout of groups of quantized local invariant descriptors as well as their relative frequency. A graph of...
Nikos Paragios, Regis Behmo, Véronique Prin...