摘要： 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. 出版者： Abingdon: Taylor & Francis 出版日期： 2015-10-03 出處： Journal of applied statistics, 2015-10, Vol.42 (10), p.2220-2232 資源來源： EBSCOhost Business Source Premier 版權： 2015 Taylor & Francis 2015 版權： Copyright Taylor & Francis Ltd. 2015 識別號： ISSN: 0266-4763 識別號： EISSN: 1360-0532 識別號： DOI: 10.1080/02664763.2015.1023271
