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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Unsupervised ranking of clustering algorithms by INFOMAX.

Sandipan Sikdar1, Animesh Mukherjee2, Matteo Marsili3

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Summary

Ranking clustering algorithms is crucial for data analysis. Linsker's Infomax principle, using partition entropy, effectively ranks clustering and community detection algorithm performance on diverse datasets.

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Area of Science:

  • Data Science
  • Machine Learning
  • Network Analysis

Background:

  • Clustering and community detection are vital for extracting insights from large datasets.
  • Numerous algorithms exist, necessitating performance evaluation methods.
  • Ranking algorithm effectiveness is essential for practical applications.

Purpose of the Study:

  • To introduce a method for ranking clustering and community detection algorithms.
  • To demonstrate the applicability of Linsker's Infomax principle for algorithm performance evaluation.
  • To validate the proposed ranking method across various datasets.

Main Methods:

  • Utilizing Linsker's Infomax principle to evaluate clustering algorithms.
  • Calculating the entropy of the partition for different algorithms.
  • Comparing algorithm rankings based on partition entropy with ground truth partitions.

Main Results:

  • Linsker's Infomax principle provides a reliable method for ranking clustering algorithms.
  • Higher partition entropy values correlate with better algorithm performance.
  • The ranking method shows strong correlation with ground truth partitions across diverse datasets.

Conclusions:

  • Partition entropy, guided by Linsker's Infomax principle, is an effective metric for ranking clustering algorithms.
  • This approach offers a robust way to select optimal algorithms for data analysis tasks.
  • The findings are validated on datasets with varying sizes and structures.