Sciweavers

Share
ECEASST
2010

Self Organized Swarms for cluster preserving Projections of high-dimensional Data

9 years 9 months ago
Self Organized Swarms for cluster preserving Projections of high-dimensional Data
: A new approach for topographic mapping, called Swarm-Organized Projection (SOP) is presented. SOP has been inspired by swarm intelligence methods for clustering and is similar to Curvilinear Component Analysis (CCA) and SOM. In contrast to the latter the choice of critical parameters is substituted by selforganization. On several crucial benchmark data sets it is demonstrated that SOP outperforms many other projection methods. SOP produces coherent clusters even for complex entangled high dimensional cluster structures. For a nontrivial dataset on protein DNA sequence Multi Dimensional Scaling (MDS) and CCA fail to represent the clusters in the data, although the clusters are clearly defined. With SOP the correct clusters in the data could be easily detected.
Alfred Ultsch, Lutz Herrmann
Added 02 Mar 2011
Updated 02 Mar 2011
Type Journal
Year 2010
Where ECEASST
Authors Alfred Ultsch, Lutz Herrmann
Comments (0)
books