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» Efficient Discovery of Confounders in Large Data Sets
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KDD
2006
ACM
142views Data Mining» more  KDD 2006»
14 years 8 months ago
Mining distance-based outliers from large databases in any metric space
Let R be a set of objects. An object o R is an outlier, if there exist less than k objects in R whose distances to o are at most r. The values of k, r, and the distance metric ar...
Yufei Tao, Xiaokui Xiao, Shuigeng Zhou
KDD
2006
ACM
150views Data Mining» more  KDD 2006»
14 years 8 months ago
Maximally informative k-itemsets and their efficient discovery
In this paper we present a new approach to mining binary data. We treat each binary feature (item) as a means of distinguishing two sets of examples. Our interest is in selecting ...
Arno J. Knobbe, Eric K. Y. Ho
BMCBI
2007
102views more  BMCBI 2007»
13 years 7 months ago
Setting up a large set of protein-ligand PDB complexes for the development and validation of knowledge-based docking algorithms
Background: The number of algorithms available to predict ligand-protein interactions is large and ever-increasing. The number of test cases used to validate these methods is usua...
Luis A. Diago, Persy Morell, Longendri Aguilera, E...
SAC
2005
ACM
14 years 1 months ago
Mining concept associations for knowledge discovery in large textual databases
In this paper, we describe a new approach for mining concept associations from large text collections. The concepts are short sequences of words that occur frequently together acr...
Xiaowei Xu, Mutlu Mete, Nurcan Yuruk
3DIM
1999
IEEE
13 years 12 months ago
Large Data Sets and Confusing Scenes in 3-D Surface Matching and Recognition
In this paper, we report on recent extensions to a surface matching algorithm based on local 3-D signatures. This algorithm was previously shown to be effective in view registrati...
Owen T. Carmichael, Daniel F. Huber, Martial Heber...