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» Approximate data mining in very large relational data
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PAMI
2010
164views more  PAMI 2010»
13 years 5 months ago
Large-Scale Discovery of Spatially Related Images
— We propose a randomized data mining method that finds clusters of spatially overlapping images. The core of the method relies on the min-Hash algorithm for fast detection of p...
Ondrej Chum, Jiri Matas
ICML
2005
IEEE
14 years 8 months ago
Core Vector Regression for very large regression problems
In this paper, we extend the recently proposed Core Vector Machine algorithm to the regression setting by generalizing the underlying minimum enclosing ball problem. The resultant...
Ivor W. Tsang, James T. Kwok, Kimo T. Lai
AAAI
1997
13 years 8 months ago
Efficient Management of Very Large Ontologies
This paper describes an environment for supporting very large ontologies. The system can be used on single PCs, workstations, a cluster of workstations, and high-end parallel supe...
Kilian Stoffel, Merwyn G. Taylor, James A. Hendler
ICDE
2010
IEEE
222views Database» more  ICDE 2010»
13 years 5 months ago
Finding Clusters in subspaces of very large, multi-dimensional datasets
Abstract— We propose the Multi-resolution Correlation Cluster detection (MrCC), a novel, scalable method to detect correlation clusters able to analyze dimensional data in the ra...
Robson Leonardo Ferreira Cordeiro, Agma J. M. Trai...
KDD
2008
ACM
120views Data Mining» more  KDD 2008»
14 years 7 months ago
Entity categorization over large document collections
Extracting entities (such as people, movies) from documents and identifying the categories (such as painter, writer) they belong to enable structured querying and data analysis ov...
Arnd Christian König, Rares Vernica, Venkates...