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ICIC
2005
Springer
15 years 5 months ago
Methods of Decreasing the Number of Support Vectors via k-Mean Clustering
This paper proposes two methods which take advantage of k -mean clustering algorithm to decrease the number of support vectors (SVs) for the training of support vector machine (SVM...
Xiao-Lei Xia, Michael R. Lyu, Tat-Ming Lok, Guang-...
PODS
2004
ACM
158views Database» more  PODS 2004»
15 years 12 months ago
k-Means Projective Clustering
Pankaj K. Agarwal, Nabil H. Mustafa
104
Voted
ISAAC
2005
Springer
122views Algorithms» more  ISAAC 2005»
15 years 5 months ago
Fast k-Means Algorithms with Constant Approximation
In this paper we study the k-means clustering problem. It is well-known that the general version of this problem is NP-hard. Numerous approximation algorithms have been proposed fo...
Mingjun Song, Sanguthevar Rajasekaran
TKDE
2008
162views more  TKDE 2008»
14 years 11 months ago
Continuous k-Means Monitoring over Moving Objects
Given a dataset P, a k-means query returns k points in space (called centers), such that the average squared distance between each point in P and its nearest center is minimized. S...
Zhenjie Zhang, Yin Yang, Anthony K. H. Tung, Dimit...
CORR
2008
Springer
158views Education» more  CORR 2008»
14 years 11 months ago
Improved Smoothed Analysis of the k-Means Method
The k-means method is a widely used clustering algorithm. One of its distinguished features is its speed in practice. Its worst-case running-time, however, is exponential, leaving...
Bodo Manthey, Heiko Röglin