Comparison of expectation-maximization clustering and logistic regression on categorization of planets with known properties
Abstract
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.
Keywords
Exoplanets; Categorizing; Comparison; Expectation-maximization clustering; Logistic Regression; Machine Learning
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PDFDOI: http://dx.doi.org/10.21533/scjournal.v5i2.120
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Copyright (c) 2016 Ajla Suljevic-Pasic, sADINA Gagula-Palalic
ISSN 2233 -1859
Digital Object Identifier DOI: 10.21533/scjournal
This work is licensed under a Creative Commons Attribution 4.0 International License