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Updated: Sep 8, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
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.
Abstract:
This paper proposes k-medoids clustering method to reveal the distinct groups of 1318 variable stars in the Galaxy based on their light curves, where each light curve represents the graph of brightness of the star against time. To overcome the deficiencies of subjective traditional classification, we separate the stars more scientifically according to their geometrical configuration and show that our approach outperforms the existing classification schemes in astronomy. It results in two optimum groups of eclipsing binaries corresponding to bright, massive systems and fainter, less massive systems.
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