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147
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EUROPAR
1999
Springer
15 years 7 months ago
Parallel k/h-Means Clustering for Large Data Sets
This paper describes the realization of a parallel version of the k/h-means clustering algorithm. This is one of the basic algorithms used in a wide range of data mining tasks. We ...
Kilian Stoffel, Abdelkader Belkoniene
114
Voted
NIPS
2004
15 years 5 months ago
Hierarchical Clustering of a Mixture Model
In this paper we propose an efficient algorithm for reducing a large mixture of Gaussians into a smaller mixture while still preserving the component structure of the original mod...
Jacob Goldberger, Sam T. Roweis
ICPR
2008
IEEE
16 years 4 months ago
Impulse noise removal by spectral clustering and regularization on graphs
In this paper we present a method for impulse noise removal that makes use of spectral clustering and graph regularization. The image is modeled as a graph and local spectral anal...
Olivier Lezoray, Vinh-Thong Ta, Abderrahim Elmoata...
109
Voted
ICPPW
2008
IEEE
15 years 10 months ago
Disparity: Scalable Anomaly Detection for Clusters
In this paper, we describe disparity, a tool that does parallel, scalable anomaly detection for clusters. Disparity uses basic statistical methods and scalable reduction operation...
Narayan Desai, Rick Bradshaw, Ewing L. Lusk
126
Voted
ICPR
2010
IEEE
15 years 9 months ago
The Detection of Concept Frames Using Clustering Multi-Instance Learning
Abstract—The classification of sequences requires the combination of information from different time points. In this paper the detection of facial expressions is considered. Exp...
David Tax, Michel Valstar