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» A New Indexing Method for High Dimensional Dataset
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DKE
2006
67views more  DKE 2006»
13 years 7 months ago
Indexed-based density biased sampling for clustering applications
Density biased sampling (DBS) has been proposed to address the limitations of Uniform sampling, by producing the desired probability distribution in the sample. The ease of produc...
Alexandros Nanopoulos, Yannis Theodoridis, Yannis ...
IPPS
1998
IEEE
13 years 11 months ago
Improved Concurrency Control Techniques For Multi-Dimensional Index Structures
Multi-dimensional index structures such as R-trees enable fast searching in high-dimensional spaces. They differ from uni-dimensional structures in the following aspects: (1) inde...
Kothuri Venkata Ravi Kanth, David Serena, Ambuj K....
PODS
1997
ACM
182views Database» more  PODS 1997»
13 years 11 months ago
A Cost Model For Nearest Neighbor Search in High-Dimensional Data Space
In this paper, we present a new cost model for nearest neighbor search in high-dimensional data space. We first analyze different nearest neighbor algorithms, present a generaliza...
Stefan Berchtold, Christian Böhm, Daniel A. K...
SISAP
2008
IEEE
188views Data Mining» more  SISAP 2008»
14 years 1 months ago
High-Dimensional Similarity Retrieval Using Dimensional Choice
There are several pieces of information that can be utilized in order to improve the efficiency of similarity searches on high-dimensional data. The most commonly used information...
Dave Tahmoush, Hanan Samet
ICDE
2000
IEEE
168views Database» more  ICDE 2000»
14 years 8 months ago
PAC Nearest Neighbor Queries: Approximate and Controlled Search in High-Dimensional and Metric Spaces
In high-dimensional and complex metric spaces, determining the nearest neighbor (NN) of a query object ? can be a very expensive task, because of the poor partitioning operated by...
Paolo Ciaccia, Marco Patella