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ICDE
2007
IEEE
228views Database» more  ICDE 2007»
12 years 2 months ago
A General Cost Model for Dimensionality Reduction in High Dimensional Spaces
Similarity search usually encounters a serious problem in the high dimensional space, known as the “curse of dimensionality”. In order to speed up the retrieval efficiency, p...
Xiang Lian, Lei Chen 0002
PODS
2001
ACM
190views Database» more  PODS 2001»
12 years 8 months ago
On the Effects of Dimensionality Reduction on High Dimensional Similarity Search
The dimensionality curse has profound e ects on the effectiveness of high-dimensional similarity indexing from the performance perspective. One of the well known techniques for im...
Charu C. Aggarwal
PODS
1997
ACM
182views Database» more  PODS 1997»
12 years 11 days 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...
KDD
2001
ACM
253views Data Mining» more  KDD 2001»
12 years 8 months ago
GESS: a scalable similarity-join algorithm for mining large data sets in high dimensional spaces
The similarity join is an important operation for mining high-dimensional feature spaces. Given two data sets, the similarity join computes all tuples (x, y) that are within a dis...
Jens-Peter Dittrich, Bernhard Seeger
VLDB
2000
ACM
229views Database» more  VLDB 2000»
11 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
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