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» Condensed Nearest Neighbor Data Domain Description
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KAIS
2007
112views more  KAIS 2007»
15 years 1 months ago
The pairwise attribute noise detection algorithm
Analyzing the quality of data prior to constructing data mining models is emerging as an important issue. Algorithms for identifying noise in a given data set can provide a good me...
Jason Van Hulse, Taghi M. Khoshgoftaar, Haiying Hu...
ICML
2007
IEEE
16 years 2 months ago
Learning to combine distances for complex representations
The k-Nearest Neighbors algorithm can be easily adapted to classify complex objects (e.g. sets, graphs) as long as a proper dissimilarity function is given over an input space. Bo...
Adam Woznica, Alexandros Kalousis, Melanie Hilario
KDD
2008
ACM
172views Data Mining» more  KDD 2008»
16 years 1 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
RECSYS
2009
ACM
15 years 8 months ago
Context-based splitting of item ratings in collaborative filtering
Collaborative Filtering (CF) recommendations are computed by leveraging a historical data set of users’ ratings for items. It assumes that the users’ previously recorded ratin...
Linas Baltrunas, Francesco Ricci
ICDE
2008
IEEE
204views Database» more  ICDE 2008»
16 years 2 months ago
Keyword Search on Spatial Databases
Many applications require finding objects closest to a specified location that contains a set of keywords. For example, online yellow pages allow users to specify an address and a ...
Ian De Felipe, Vagelis Hristidis, Naphtali Rishe