434 search results - page 5 / 87 » Dimensionality Reduction of Clustered Data Sets |
DIS
2006
Springer
13 years 11 months ago
2006
Springer
Parametric Embedding (PE) has recently been proposed as a general-purpose algorithm for class visualisation. It takes class posteriors produced by a mixture-based clustering algori...
CVPR
2008
IEEE
14 years 9 months ago
2008
IEEE
We propose a novel algorithm for clustering data sampled from multiple submanifolds of a Riemannian manifold. First, we learn a representation of the data using generalizations of...
SIAMSC
2008
13 years 7 months ago
2008
Coding of data, usually upstream of data analysis, has crucial implications for the data analysis results. By modifying the data coding
VLDB
1999
ACM
1999
ACM
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
13 years 11 months ago
Many applications require the clustering of large amounts of high-dimensional data. Most clustering algorithms, however, do not work e ectively and e ciently in highdimensional sp...
ICDE
2003
IEEE
14 years 8 months ago
2003
IEEE
The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well known approach to o...