Fuzzy nonlinear projection

Author

Runkler, Thomas A.

Author_Institution

Inf. & Commun. Neural Comput. Dept., Siemens AG, Munich, Germany

Volume

2

fYear

2003

Firstpage

863

Abstract

The objective functions for nonlinear projection and clustering are combined and lead to the definition of fuzzy nonlinear projection. Conventional nonlinear projection preserves topologies well, but produces bad results for multiple manifolds. Conventional clustering can discover complex cluster shapes, but the geometry has to be specified in advance. Fuzzy nonlinear projection avoids these drawbacks of projection and clustering methods. It produces both good projections and good partitions for data sets that contain arbitrarily shaped multiple nonlinear manifolds.

Keywords

fuzzy systems; optimisation; pattern clustering; principal component analysis; topology; arbitrarily shaped multiple nonlinear manifolds; clustering methods; complex cluster shapes; conventional clustering; conventional nonlinear projection; data sets; fuzzy nonlinear projection; good partitions; good projections; nonlinear clustering; projection methods; topology; Clustering algorithms; Clustering methods; Communications technology; Covariance matrix; Fuzzy systems; Geometry; Partitioning algorithms; Principal component analysis; Shape; Topology;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on

Print_ISBN

0-7803-7810-5

Type

conf

DOI

10.1109/FUZZ.2003.1206544

Filename

1206544