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» On Approximating the Radii of Point Sets in High Dimensions
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SIGMOD
2002
ACM
246views Database» more  SIGMOD 2002»
14 years 7 months ago
Hierarchical subspace sampling: a unified framework for high dimensional data reduction, selectivity estimation and nearest neig
With the increased abilities for automated data collection made possible by modern technology, the typical sizes of data collections have continued to grow in recent years. In suc...
Charu C. Aggarwal
TKDE
2011
332views more  TKDE 2011»
13 years 2 months ago
Adaptive Cluster Distance Bounding for High-Dimensional Indexing
—We consider approaches for similarity search in correlated, high-dimensional data-sets, which are derived within a clustering framework. We note that indexing by “vector appro...
Sharadh Ramaswamy, Kenneth Rose
SODA
2000
ACM
127views Algorithms» more  SODA 2000»
13 years 9 months ago
Dimensionality reduction techniques for proximity problems
In this paper we give approximation algorithms for several proximity problems in high dimensional spaces. In particular, we give the rst Las Vegas data structure for (1 + )-neares...
Piotr Indyk
VLDB
2000
ACM
229views Database» more  VLDB 2000»
13 years 11 months ago
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
Many emerging application domains require database systems to support efficient access over highly multidimensional datasets. The current state-of-the-art technique to indexing hi...
Kaushik Chakrabarti, Sharad Mehrotra
KDD
2001
ACM
192views Data Mining» more  KDD 2001»
14 years 8 months ago
Data mining with sparse grids using simplicial basis functions
Recently we presented a new approach [18] to the classification problem arising in data mining. It is based on the regularization network approach but, in contrast to other method...
Jochen Garcke, Michael Griebel