Portal de Periódicos da CAPES

Comparison of expectation-maximization clustering and logistic regression on categorization of planets with known properties

2016; International University of Sarajevo; Volume: 5; Issue: 2 Linguagem: Inglês

10.21533/scjournal.v5i2.120

2233-1859

Ajla Suljevic-Pasic, Sadina Gagula-Palalic,

Stellar, planetary, and galactic studies

Analysis of the exoplanet data is the top priority of astrophysicists today. With the increasing incoming information there is a need for an efficient and reliable algorithm. The data is taken from exoplanet data explorer which was cross checked and filtered with NASA’s known categorization. These were then sorted into 5 categories: Dwarfs, Terrestrial, Icy, Jovian and Giant planets. This paper compares expectation-maximization clustering algorithm as an unsupervised and logistic regression as a supervised machine learning methodologies. Comparatively, logistic regression outperformed EM, indicating it cannot be used to sort through the incoming data. Further analysis is necessary.