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» Approximate data mining in very large relational data
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129
Voted
MP
2010
162views more  MP 2010»
15 years 4 days ago
Approximation accuracy, gradient methods, and error bound for structured convex optimization
Convex optimization problems arising in applications, possibly as approximations of intractable problems, are often structured and large scale. When the data are noisy, it is of i...
Paul Tseng
118
Voted
ICDM
2009
IEEE
125views Data Mining» more  ICDM 2009»
15 years 8 months ago
A Fully Automated Method for Discovering Community Structures in High Dimensional Data
—Identifying modules, or natural communities, in large complex networks is fundamental in many fields, including social sciences, biological sciences and engineering. Recently s...
Jianhua Ruan
KDD
1998
ACM
190views Data Mining» more  KDD 1998»
15 years 6 months ago
Time Series Forecasting from High-Dimensional Data with Multiple Adaptive Layers
This paper describes our work in learning online models that forecast real-valued variables in a high-dimensional space. A 3GB database was collected by sampling 421 real-valued s...
R. Bharat Rao, Scott Rickard, Frans Coetzee
ESANN
2008
15 years 3 months ago
Parallelizing single patch pass clustering
Clustering algorithms such as k-means, the self-organizing map (SOM), or Neural Gas (NG) constitute popular tools for automated information analysis. Since data sets are becoming l...
Nikolai Alex, Barbara Hammer
VLDB
1999
ACM
118views Database» more  VLDB 1999»
15 years 6 months ago
Similarity Search in High Dimensions via Hashing
The nearest- or near-neighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing ...
Aristides Gionis, Piotr Indyk, Rajeev Motwani