Title :
The kernel self-organising map
Author :
MacDonald, Donald ; Fyfe, Colin
Author_Institution :
Appl. Comput. Intelligence Res. Unit, Paisley Univ., UK
Abstract :
We review a recently-developed method of performing k-means clustering in a high-dimensional feature space and extend it to give the resultant mapping topology-preserving properties. We show the results of the new algorithm on the standard data set, on random numbers drawn uniformly from [0,1)2 and on the Olivetti database of faces. The new algorithm converges extremely quickly
Keywords :
convergence; pattern clustering; self-organising feature maps; topology; Olivetti face database; convergence rate; high-dimensional feature space; k-means clustering; kernel self-organising map; mapping topology-preserving properties; random numbers; standard data set; Algorithm design and analysis; Clustering algorithms; Convergence; Equations; Euclidean distance; Intelligent systems; Kernel; Neurons; Space technology; Topology;
Conference_Titel :
Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on
Conference_Location :
Brighton
Print_ISBN :
0-7803-6400-7
DOI :
10.1109/KES.2000.885820
