Title :
A levelwise search algorithm for interesting subspace clusters
Author_Institution :
Cincinnati Univ., OH, USA
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;
Conference_Titel :
Data Mining, Fifth IEEE International Conference on
Print_ISBN :
0-7695-2278-5
DOI :
10.1109/ICDM.2005.9
