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Clusterdv: a simple density-based clustering method that is robust, general and automatic.

João C Marques1,2, Michael B Orger1

  • 1Champalimaud Research, Champalimaud Centre for the Unknown, Avenida Brasília, Doca de Pedrouços, Lisboa, Portugal.

Bioinformatics (Oxford, England)
|November 9, 2018
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Summary
This summary is machine-generated.

A new method, clusterdv, automatically partitions datasets into clusters by estimating density dips. This robust approach works across diverse data types without manual parameter tuning, outperforming existing methods.

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Area of Science:

  • Data Science
  • Computational Biology
  • Machine Learning

Background:

  • Dataset partitioning into distinct clusters is a challenging problem due to variations in data features like shape, number, density, noise, and overlap.
  • Existing methods, such as clusterdp based on density peaks, are not fully automatic and can fail on simple data distributions.

Purpose of the Study:

  • To introduce clusterdv, an automated approach for robust dataset clustering.
  • To overcome limitations of existing methods by enabling automatic determination of cluster number and distribution.

Main Methods:

  • The clusterdv method estimates density dips between data points to identify cluster boundaries.
  • This approach requires no manual parameter adjustment, making it fully automatic.

Main Results:

  • clusterdv successfully clusters a range of synthetic and experimental datasets with known underlying structures.
  • The method demonstrates consistent and meaningful cluster identification in novel behavioral data.

Conclusions:

  • clusterdv offers a robust and automated solution for dataset clustering across diverse data types.
  • The method provides a valuable tool for data analysis in various scientific domains.