113 search results - page 1 / 23 » Locally adaptive metrics for clustering high dimensional dat... |
DATAMINE
2007
13 years 4 months ago
2007
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...
ICML
1995
IEEE
14 years 5 months ago
1995
IEEE
Understanding high-dimensional real world data usually requires learning the structure of the data space. The structure maycontain high-dimensional clusters that are related in co...
PAKDD
2009
ACM
13 years 11 months ago
2009
ACM
Abstract. Clustering high dimensional data with sparse features is challenging because pairwise distances between data items are not informative in high dimensional space. To addre...
ECML
2006
Springer
13 years 8 months ago
2006
Springer
A critical problem in clustering research is the definition of a proper metric to measure distances between points. Semi-supervised clustering uses the information provided by the ...