Medizinische Universität Graz - Research portal
Selected Publication:
Reibnegger, G; Wachter, H.
Self-organizing neural networks--an alternative way of cluster analysis in clinical chemistry.
Clin Chim Acta. 1996; 248(1):91-98
Doi: 10.1016%2F0009-8981%2895%2906269-6
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- Leading authors Med Uni Graz
- Reibnegger Gilbert
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- Abstract:
- Supervised learning schemes have been employed by several workers for training neural networks designed to solve clinical problems. We demonstrate that unsupervised techniques can also produce interesting and meaningful results. Using a data set on the chemical composition of milk from 22 different mammals, we demonstrate that self-organizing feature maps (Kohonen networks) as well as a modified version of error backpropagation technique yield results mimicking conventional cluster analysis. Both techniques are able to project a potentially multi-dimensional input vector onto a two-dimensional space whereby neighborhood relationships remain conserved. Thus, these techniques can be used for reducing dimensionality of complicated data sets and for enhancing comprehensibility of features hidden in the data matrix.
- Find related publications in this database (using NLM MeSH Indexing)
- Animals -
- Chemistry, Clinical - methods
- Cluster Analysis - methods
- Data Interpretation, Statistical - methods
- Milk - chemistry
- Neural Networks (Computer) - chemistry
- Find related publications in this database (Keywords)
- Neural Networks
- Connectionist Models
- Cluster Analysis
- Self-Organizing Learning Schemes
- Unsupervised Learning Schemes