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COMPGEOM
2006
ACM
15 years 6 months ago
A fast k-means implementation using coresets
In this paper we develop an efficient implementation for a k-means clustering algorithm. The novel feature of our algorithm is that it uses coresets to speed up the algorithm. A ...
Gereon Frahling, Christian Sohler
NIPS
2003
15 years 1 months ago
Clustering with the Connectivity Kernel
Clustering aims at extracting hidden structure in dataset. While the problem of finding compact clusters has been widely studied in the literature, extracting arbitrarily formed ...
Bernd Fischer, Volker Roth, Joachim M. Buhmann
95
Voted
KAIS
2006
126views more  KAIS 2006»
15 years 11 days ago
Fast and exact out-of-core and distributed k-means clustering
Clustering has been one of the most widely studied topics in data mining and k-means clustering has been one of the popular clustering algorithms. K-means requires several passes ...
Ruoming Jin, Anjan Goswami, Gagan Agrawal
102
Voted
VISSYM
2003
15 years 1 months ago
Adaptive Smooth Scattered Data Approximation for Large-scale Terrain Visualization
We present a fast method that adaptively approximates large-scale functional scattered data sets with hierarchical B-splines. The scheme is memory efficient, easy to implement an...
Martin Bertram, Xavier Tricoche, Hans Hagen
224
Voted

Book
4675views
16 years 9 months ago
Mathematics of The Discrete Fourier Transform (DFT) with Audio Applications
"The Discrete Fourier Transform (DFT) can be understood as a numerical approximation to the Fourier transform. However, the DFT has its own exact Fourier theory, which is the ...
Julius O. Smith III