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» Spectral Clustering with Perturbed Data
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NIPS
2003
13 years 9 months ago
Pairwise Clustering and Graphical Models
Significant progress in clustering has been achieved by algorithms that are based on pairwise affinities between the datapoints. In particular, spectral clustering methods have ...
Noam Shental, Assaf Zomet, Tomer Hertz, Yair Weiss
CSDA
2006
67views more  CSDA 2006»
13 years 7 months ago
Sensitivity analysis of the strain criterion for multidimensional scaling
Multidimensional scaling (MDS) is a collection of data analytic techniques for constructing configurations of points from dissimilarity information about interpoint distances. Cla...
R. M. Lewis, M. W. Trosset
DIS
2006
Springer
13 years 11 months ago
Clustering Pairwise Distances with Missing Data: Maximum Cuts Versus Normalized Cuts
Abstract. Clustering algorithms based on a matrix of pairwise similarities (kernel matrix) for the data are widely known and used, a particularly popular class being spectral clust...
Jan Poland, Thomas Zeugmann
SDM
2009
SIAM
225views Data Mining» more  SDM 2009»
14 years 4 months ago
Integrated KL (K-means - Laplacian) Clustering: A New Clustering Approach by Combining Attribute Data and Pairwise Relations.
Most datasets in real applications come in from multiple sources. As a result, we often have attributes information about data objects and various pairwise relations (similarity) ...
Fei Wang, Chris H. Q. Ding, Tao Li
ICIP
2007
IEEE
14 years 9 months ago
Unsupervised Modeling of Object Tracks for Fast Anomaly Detection
A key goal of far-field activity analysis is to learn the usual pattern of activity in a scene and to detect statistically anomalous behavior. We propose a method for unsupervised...
Tomas Izo, W. Eric L. Grimson