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» High Dimensional Similarity Joins: Algorithms and Performanc...
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KDD
2008
ACM
172views Data Mining» more  KDD 2008»
15 years 10 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
DEXA
2006
Springer
151views Database» more  DEXA 2006»
14 years 11 months ago
An Incremental Refining Spatial Join Algorithm for Estimating Query Results in GIS
Geographic information systems (GIS) must support large georeferenced data sets. Due to the size of these data sets finding exact answers to spatial queries can be very time consum...
Wan D. Bae, Shayma Alkobaisi, Scott T. Leutenegger
DMSN
2005
ACM
14 years 11 months ago
The threshold join algorithm for top-k queries in distributed sensor networks
In this paper we present the Threshold Join Algorithm (TJA), which is an efficient TOP-k query processing algorithm for distributed sensor networks. The objective of a top-k query...
Demetrios Zeinalipour-Yazti, Zografoula Vagena, Di...
CEC
2009
IEEE
15 years 4 months ago
Tackling high dimensional nonseparable optimization problems by cooperatively coevolving particle swarms
— This paper attempts to address the question of scaling up Particle Swarm Optimization (PSO) algorithms to high dimensional optimization problems. We present a cooperative coevo...
Xiaodong Li, Xin Yao
SSD
2009
Springer
124views Database» more  SSD 2009»
15 years 4 months ago
ELKI in Time: ELKI 0.2 for the Performance Evaluation of Distance Measures for Time Series
ELKI is a unied software framework, designed as a tool suitable for evaluation of dierent algorithms on high dimensional realvalued feature-vectors. A special case of high dimens...
Elke Achtert, Thomas Bernecker, Hans-Peter Kriegel...