Supervised Neuro-fuzzy Clustering for Life Science Applications

  • Jürgen Paetz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4345)


Classification, clustering and rule generation are important tasks in multidimensional data analysis. The combination of clustering or classification with rule generation gives an explanation for the achieved results. Especially in life science applications experts are interested in explanations to understand the underlying data. The usage of supervised neuro-fuzzy systems is a suitable approach for this combined task. Not always classification labels are available for the data when considering new problem areas in life science. Since we had already used a supervised neuro-fuzzy system for some applications, our aim in the case studies was to use the same neuro-fuzzy classifier for clustering, generating understandable rules also for clusters. To do so, we added Monte-Carlo random data to the original data and performed the clustering task with the present classifier in the medical, chemical, and biological domain.


Class Label Virtual Screening Test Frequency Septic Shock Patient Cluster Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jürgen Paetz
    • 1
  1. 1.J.W. Goethe-Universität Frankfurt am MainFrankfurt am MainGermany

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