165 search results - page 1 / 33 » Adaptive dimension reduction for clustering high dimensional... |
ICDM
2002
IEEE
13 years 9 months ago
2002
IEEE
It is well-known that for high dimensional data clustering, standard algorithms such as EM and the K-means are often trapped in local minimum. Many initialization methods were pro...
ICDE
2003
IEEE
14 years 5 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...
VLDB
2007
ACM
14 years 4 months ago
2007
ACM
Abstract The notorious "dimensionality curse" is a wellknown phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approa...
VISSYM
2003
13 years 5 months ago
2003
Traditional visualization techniques for multidimensional data sets, such as parallel coordinates, glyphs, and scatterplot matrices, do not scale well to high numbers of dimension...
VLDB
2000
ACM
13 years 7 months ago
2000
ACM
Many emerging application domains require database systems to support efficient access over highly multidimensional datasets. The current state-of-the-art technique to indexing hi...