Categorizing Stars with Known Properties Using the Expectation-Maximization Clustering Algorithm

Ajla Suljevic-Pasic, Emine Yaman


Hertzsprung and Russell, created a diagram of then known stars with respect to absolute magnitudes or luminosities versus their stellar classifications or effective temperatures. This gave a clear clusters of star types, namely main sequence stars, from birth to maturity, followed by giants, supergiants and white dwarfs. With the rise of technology number of stars with known properties had been growing exponentially and manual categorization is futile. Using the same parameters of HR diagram, this paper analyzes the efficiency of unsupervised ML algorithm expectation-maximization clustering on a database containing 120 000 stars.


Stars; Categorizing; Hertzsprung-Russell diagram; Comparison; Expectation-maximization clustering; Machine Learning

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Copyright (c) 2018 Ajla Suljevic-Pasic, Emine Yaman

ISSN 2233 -1859

Digital Object Identifier DOI: 10.21533/scjournal

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License