An intuitive clustering algorithm for spherical data with application to extrasolar planets

This paper proposes an intuitive clustering algorithm capable of automatically self-organizing data groups based on the original data structure. Comparisons between the propopsed algorithm and EM [ 1 ] and spherical k-means [ 7 ] algorithms are given. These numerical results show the effectiveness o...

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Bibliographic Details
Published in:Journal of applied statistics 2015-10, Vol.42 (10), p.2220-2232
Main Authors: Hung, Wen-Liang, Chang-Chien, Shou-Jen, Yang, Miin-Shen
Format: Article
Language:English
Subjects:
Algorithms
Applied statistics
Astronomers
Classification
Clustering
Data structures
Disks
EM algorithm
Extrasolar planets
mixtures of von mises distributions
Planetary evolution
Planetary systems
spherical data
spherical k-means algorithm
Studies
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Summary:This paper proposes an intuitive clustering algorithm capable of automatically self-organizing data groups based on the original data structure. Comparisons between the propopsed algorithm and EM [ 1 ] and spherical k-means [ 7 ] algorithms are given. These numerical results show the effectiveness of the proposed algorithm, using the correct classification rate and the adjusted Rand index as evaluation criteria [ 5 , 6 ]. In 1995, Mayor and Queloz announced the detection of the first extrasolar planet (exoplanet) around a Sun-like star. Since then, observational efforts of astronomers have led to the detection of more than 1000 exoplanets. These discoveries may provide important information for understanding the formation and evolution of planetary systems. The proposed clustering algorithm is therefore used to study the data gathered on exoplanets. Two main implications are also suggested: (1) there are three major clusters, which correspond to the exoplanets in the regimes of disc, ongoing tidal and tidal interactions, respectively, and (2) the stellar metallicity does not play a key role in exoplanet migration.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2015.1023271