Model-Based Cluster Analysis Applied to Flow Cytometry Data

  • Ute Simon
  • Hans-Joachim Mucha
  • Rainer Brüggemann
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Flow cytometry is an appropriate technique for the investigation and monitoring of phytoplankton (algae), providing quick, semi-automatic single-cell analysis. However, to use flow cytometry in phytoplankton research routinely, an objective and automated i.e. computer-supported data analysis is demanded. For this reason, in a pilot study a sequence of different steps of cluster analysis has been developed, including model-based and hierarchical clustering, as well as the concept of cores and weighting of observations and parameters. A successful application of the method is demonstrated for a snapshot of a sample of Lake Müggelsee in Berlin (Germany).


Green Alga Bayesian Information Criterion Algal Cell Radial Basis Function Neural Network Determinant Criterion 
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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Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Ute Simon
    • 1
  • Hans-Joachim Mucha
    • 2
  • Rainer Brüggemann
    • 1
  1. 1.Leibniz-Institute of Freshwater Ecology and Inland FisheriesBerlinGermany
  2. 2.Weierstraß-Institute of Applied Analysis and StochasticBerlinGermany

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