Title :
Fuzzy nonlinear projection
Author :
Runkler, Thomas A.
Author_Institution :
Inf. & Commun. Neural Comput. Dept., Siemens AG, Munich, Germany
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;
Conference_Titel :
Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on
Print_ISBN :
0-7803-7810-5
DOI :
10.1109/FUZZ.2003.1206544
