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Updated: Sep 8, 2025
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
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Unsupervised classification of eclipsing binary light curves through k-medoids clustering.
Soumita Modak1, Tanuka Chattopadhyay2, Asis Kumar Chattopadhyay1
1Department of Statistics, University of Calcutta, Kolkata, India.
Journal of Applied Statistics
|June 16, 2022
Summary
This study uses k-medoids clustering to classify 1318 variable stars based on light curve data. The new method scientifically groups stars, outperforming traditional astronomical classification schemes.
Area of Science:
- Astronomy
- Stellar Astrophysics
- Data Science
Background:
- Traditional classification of variable stars can be subjective.
- Variable stars are crucial for understanding galactic structure and evolution.
- Light curves provide rich information about stellar behavior.
Purpose of the Study:
- To develop an objective and scientifically rigorous method for classifying variable stars.
- To group 1318 variable stars in the Galaxy using their light curve data.
- To compare the proposed method with existing astronomical classification schemes.
Main Methods:
- Application of the k-medoids clustering algorithm.
- Analysis of stellar light curves as geometrical configurations.
- Comparative evaluation against established astronomical classification techniques.
Main Results:
- Successful identification of distinct groups within the 1318 variable stars.
- The k-medoids approach demonstrates superior performance compared to existing methods.
- Two optimal groups of eclipsing binaries were identified: bright, massive systems and fainter, less massive systems.
Conclusions:
- The k-medoids clustering method offers a more scientific and objective approach to variable star classification.
- This data-driven method enhances our ability to categorize stars based on their intrinsic properties.
- The findings provide a refined understanding of eclipsing binary populations in the Galaxy.
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