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128
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SIGMOD
2001
ACM
200views Database» more  SIGMOD 2001»
16 years 25 days ago
Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering
In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or ra...
Markus M. Breunig, Hans-Peter Kriegel, Peer Kr&oum...
108
Voted
BMCBI
2008
142views more  BMCBI 2008»
15 years 24 days ago
Genetic weighted k-means algorithm for clustering large-scale gene expression data
Background: The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It...
Fang-Xiang Wu
88
Voted
ICASSP
2010
IEEE
15 years 26 days ago
An adaptive initialization method for speaker Diarization based on prosodic features
The following article presents a novel, adaptive initialization scheme that can be applied to most state-of-the-art Speaker Diarization algorithms, i.e. algorithms that use agglom...
David Imseng, Gerald Friedland
110
Voted
ICRA
2008
IEEE
297views Robotics» more  ICRA 2008»
15 years 7 months ago
Fast 3D reconstruction of human shape and motion tracking by parallel fast level set method
— This paper presents a parallel algorithm of the Level Set Method named the Parallel Fast Level Set Method, and its application for real-time 3D reconstruction of human shape an...
Yumi Iwashita, Ryo Kurazume, Kenji Hara, Seiichi U...
88
Voted
CVPR
1997
IEEE
16 years 2 months ago
Tracking non-rigid, moving objects based on color cluster flow
In this contribution we present an algorithm for tracking non-rigid, moving objects in a sequence of colored images, which were recorded by a non-stationary camera. The applicatio...
Bernd Heisele, Ulrich Kressel, W. Ritter