Data-Driven Cluster Reinforcement and Visualization in Sparsely-Matched Self-Organizing Maps


TitleData-Driven Cluster Reinforcement and Visualization in Sparsely-Matched Self-Organizing Maps
Publication TypeJournal Article
Year of Publication2012
AuthorsManukyan, N, Eppstein, MJ, Rizzo, DM
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume23
Issue5
Pagination846 - 852
Date Published05/2012
ISSN2162-237X
Abstract

A self-organizing map (SOM) is a self-organized projection of high-dimensional data onto a typically 2-dimensional (2-D) feature map, wherein vector similarity is implicitly translated into topological closeness in the 2-D projection. However, when there are more neurons than input patterns, it can be challenging to interpret the results, due to diffuse cluster boundaries and limitations of current methods for displaying interneuron distances. In this brief, we introduce a new cluster reinforcement (CR) phase for sparsely-matched SOMs. The CR phase amplifies within-cluster similarity in an unsupervised, data-driven manner. Discontinuities in the resulting map correspond to between-cluster distances and are stored in a boundary (B) matrix. We describe a new hierarchical visualization of cluster boundaries displayed directly on feature maps, which requires no further clustering beyond what was implicitly accomplished during self-organization in SOM training. We use a synthetic benchmark problem and previously published microbial community profile data to demonstrate the benefits of the proposed methods.

URLhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6178802
DOI10.1109/TNNLS.2012.2190768
Short TitleIEEE Trans. Neural Netw. Learning Syst.
Status: 
Published
Attributale Grant: 
RACC
Grant Year: 
Year2