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» Feature Selection in Clustering Problems
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143
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JCSS
2002
199views more  JCSS 2002»
15 years 2 months ago
A Constant-Factor Approximation Algorithm for the k-Median Problem
We present the first constant-factor approximation algorithm for the metric k-median problem. The k-median problem is one of the most well-studied clustering problems, i.e., those...
Moses Charikar, Sudipto Guha, Éva Tardos, D...
131
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FUIN
2010
268views more  FUIN 2010»
14 years 9 months ago
Boruta - A System for Feature Selection
Machine learning methods are often used to classify objects described by hundreds of attributes; in many applications of this kind a great fraction of attributes may be totally irr...
Miron B. Kursa, Aleksander Jankowski, Witold R. Ru...
131
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INFORMS
1998
100views more  INFORMS 1998»
15 years 2 months ago
Feature Selection via Mathematical Programming
The problem of discriminating between two nite point sets in n-dimensional feature space by a separating plane that utilizes as few of the features as possible, is formulated as a...
Paul S. Bradley, Olvi L. Mangasarian, W. Nick Stre...
85
Voted
ICCBR
2005
Springer
15 years 8 months ago
A Knowledge-Intensive Method for Conversational CBR
In conversational case-based reasoning (CCBR), a main problem is how to select the most discriminative questions and display them to users in a natural way to alleviate users’ co...
Mingyang Gu, Agnar Aamodt
114
Voted
KDD
2006
ACM
112views Data Mining» more  KDD 2006»
16 years 3 months ago
K-means clustering versus validation measures: a data distribution perspective
K-means is a widely used partitional clustering method. While there are considerable research efforts to characterize the key features of K-means clustering, further investigation...
Hui Xiong, Junjie Wu, Jian Chen