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» A New Indexing Method for High Dimensional Dataset
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WSC
2008
13 years 10 months ago
On the approximation error in high dimensional model representation
Mathematical models are often described by multivariate functions, which are usually approximated by a sum of lower dimensional functions. A major problem is the approximation err...
Xiaoqun Wang
SIGMOD
2006
ACM
110views Database» more  SIGMOD 2006»
14 years 8 months ago
Finding k-dominant skylines in high dimensional space
Given a d-dimensional data set, a point p dominates another point q if it is better than or equal to q in all dimensions and better than q in at least one dimension. A point is a ...
Chee Yong Chan, H. V. Jagadish, Kian-Lee Tan, Anth...
KDD
2001
ACM
253views Data Mining» more  KDD 2001»
14 years 9 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
1999
ACM
131views Database» more  VLDB 1999»
14 years 22 days ago
High-Performance Extensible Indexing
Today’s object-relational DBMSs (ORDBMSs) are designed to support novel application domains by providing an extensible architecture, supplemented by domain-specific database ex...
Marcel Kornacker
ICDE
2012
IEEE
343views Database» more  ICDE 2012»
11 years 11 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