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SIGMOD
2000
ACM
165views Database» more  SIGMOD 2000»
14 years 2 days ago
Finding Generalized Projected Clusters In High Dimensional Spaces
High dimensional data has always been a challenge for clustering algorithms because of the inherent sparsity of the points. Recent research results indicate that in high dimension...
Charu C. Aggarwal, Philip S. Yu
IEEEVAST
2010
13 years 2 months ago
Finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological opera
Data sets in astronomy are growing to enormous sizes. Modern astronomical surveys provide not only image data but also catalogues of millions of objects (stars, galaxies), each ob...
Bilkis J. Ferdosi, Hugo Buddelmeijer, Scott Trager...
CVPR
2009
IEEE
1133views Computer Vision» more  CVPR 2009»
15 years 2 months ago
Sparse Subspace Clustering
We propose a method based on sparse representation (SR) to cluster data drawn from multiple low-dimensional linear or affine subspaces embedded in a high-dimensional space. Our ...
Ehsan Elhamifar, René Vidal
ICCV
2009
IEEE
1119views Computer Vision» more  ICCV 2009»
15 years 21 days ago
Spectral clustering of linear subspaces for motion segmentation
This paper studies automatic segmentation of multiple motions from tracked feature points through spectral embedding and clustering of linear subspaces. We show that the dimensi...
Fabien Lauer, Christoph Schn¨orr
ICDE
2005
IEEE
132views Database» more  ICDE 2005»
14 years 1 months ago
CLICKS: Mining Subspace Clusters in Categorical Data via K-partite Maximal Cliques
We present a novel algorithm called CLICKS, that finds clusters in categorical datasets based on a search for kpartite maximal cliques. Unlike previous methods, CLICKS mines subs...
Mohammed Javeed Zaki, Markus Peters