DocumentCode
2865447
Title
A levelwise search algorithm for interesting subspace clusters
Author
Bian, Hao
Author_Institution
Cincinnati Univ., OH, USA
fYear
2005
fDate
27-30 Nov. 2005
Abstract
We present a levelwise search algorithm for finding subspace clusters in high dimensional data satisfying various properties besides the commonly used minimum density property. A set of such properties are summarized and a user can choose any of these properties. A lattice is built with all the discovered clusters which enables further analysis and discovery of useful knowledge about the clusters and their inter-relationships.
Keywords
pattern clustering; search problems; high dimensional data; interesting subspace clusters; levelwise search algorithm; minimum density property; Algorithm design and analysis; Association rules; Clustering algorithms; Data mining; Gene expression; Lattices; Machine learning; Machine learning algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining, Fifth IEEE International Conference on
ISSN
1550-4786
Print_ISBN
0-7695-2278-5
Type
conf
DOI
10.1109/ICDM.2005.9
Filename
1565729
Link To Document