Anzeige
Erschienen in:
2005 | OriginalPaper | Buchkapitel
Clustering and Metaclustering with Nonnegative Matrix Decompositions
verfasst von : Liviu Badea
Erschienen in: Machine Learning: ECML 2005
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Although very widely used in unsupervised data mining, most clustering methods are affected by the instability of the resulting clusters w.r.t. the initialization of the algorithm (as e.g. in k-means). Here we show that this problem can be elegantly and efficiently tackled by
meta-clustering
the clusters produced in several different runs of the algorithm, especially if “
soft
” clustering algorithms (such as Nonnegative Matrix Factorization) are used both at the object- and the meta-level. The essential difference w.r.t. other meta-clustering approaches consists in the fact that our algorithm detects frequently occurring
sub
-clusters (rather than
complete
clusters) in the various runs, which allows it to outperform existing algorithms. Additionally, we show how to perform
two-way meta
-clustering, i.e. take both object and sample dimensions of clusters simultaneously into account, a feature which is essential e.g. for
bi
clustering gene expression data, but has not been considered before.