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» Embedding a set of rational points in lower dimensions
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PR
2007
100views more  PR 2007»
13 years 8 months ago
Linear manifold clustering in high dimensional spaces by stochastic search
Classical clustering algorithms are based on the concept that a cluster center is a single point. Clusters which are not compact around a single point are not candidates for class...
Robert M. Haralick, Rave Harpaz
COMPGEOM
2010
ACM
14 years 1 months ago
Orthogonal range reporting: query lower bounds, optimal structures in 3-d, and higher-dimensional improvements
Orthogonal range reporting is the problem of storing a set of n points in d-dimensional space, such that the k points in an axis-orthogonal query box can be reported efficiently. ...
Peyman Afshani, Lars Arge, Kasper Dalgaard Larsen
COMPGEOM
2010
ACM
14 years 1 months ago
Incidences in three dimensions and distinct distances in the plane
d Abstract] György Elekes Eötvös University Micha Sharir Tel Aviv University and New York University We first describe a reduction from the problem of lower-bounding the numbe...
György Elekes, Micha Sharir
IWPEC
2009
Springer
14 years 3 months ago
The Parameterized Complexity of Some Geometric Problems in Unbounded Dimension
We study the parameterized complexity of the following fundamental geometric problems with respect to the dimension d: i) Given n points in Rd, compute their minimum enclosing cyl...
Panos Giannopoulos, Christian Knauer, Günter ...
SWAT
2010
Springer
282views Algorithms» more  SWAT 2010»
14 years 1 months ago
Better Bounds on Online Unit Clustering
Unit Clustering is the problem of dividing a set of points from a metric space into a minimal number of subsets such that the points in each subset are enclosable by a unit ball. W...
Martin R. Ehmsen, Kim S. Larsen