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Related Experiment Videos

Comparison of five clustering algorithms to classify phytoplankton from flow cytometry data.

M F Wilkins1, S A Hardy, L Boddy

  • 1Cardiff School of Biosciences, Cardiff, United Kingdom.

Cytometry
|June 29, 2001
PubMed
Summary

The adaptive distances algorithm, a fuzzy k-means variant, reliably clusters analytical flow cytometric (AFC) data from marine phytoplankton. This method improves upon existing algorithms, though automatic cluster number determination needs further research.

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Area of Science:

  • Aquatic ecology
  • Computational biology
  • Data analysis

Background:

  • Analytical flow cytometry (AFC) data present clustering challenges for artificial neural networks (ANNs).
  • Fuzzy k-means algorithm variants were developed to handle nonspherical clusters using scatter matrices.
  • Four variants were proposed, each optimizing different clustering quality measures.

Purpose of the Study:

  • To compare the performance of four fuzzy k-means algorithm variants for clustering AFC data.
  • To evaluate the effectiveness of these variants against a critical distances algorithm.
  • To identify a robust clustering method for ANN training data in aquatic ecology.

Main Methods:

  • Four fuzzy k-means algorithm variants were implemented.
  • The adaptive distances (Gustafson--Kessel) algorithm was among the variants tested.

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  • Performance was evaluated using AFC data from cultured marine phytoplankton species.

    • Main Results:

    • The adaptive distances (Gustafson--Kessel) algorithm demonstrated robustness and reliability.
    • Other fuzzy k-means variants exhibited various issues during analysis.
    • The adaptive distances algorithm outperformed the critical distances algorithm.

    Conclusions:

    • The adaptive distances algorithm is a superior choice for clustering AFC data compared to tested alternatives.
    • Further research is needed to address the automatic determination of the optimal number of clusters.
    • This finding aids in the automated extraction of clusters for ANN training data in aquatic ecology.