کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6904610 862805 2016 47 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An unsupervised clustering algorithm for data on the unit hypersphere
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
An unsupervised clustering algorithm for data on the unit hypersphere
چکیده انگلیسی
Directional data on a hypersphere has been used in biology, geology, medicine, meteorology and oceanography. Clustering is a useful tool for the analysis of these data on the unit hypersphere. In general, the EM algorithm with a mixture of von Mises distributions is the most commonly used clustering method for 2-dimensional directional data on the plane. However, the EM algorithm is sensitive to initialization, meaning the number of clusters needs to be assigned a priori. This study proposes an effectively unsupervised approach to clustering for these directional data on the unit hypersphere. The proposed clustering method is free of initialization. Without the need to assign the number of clusters, it becomes an unsupervised clustering method for the analysis of data on the unit hypersphere. Some numerical and real examples are given with comparisons to demonstrate the effectiveness and superiority of the proposed method. Finally, the proposed clustering algorithm is applied to cluster exoplanet data of extrasolar planets. The clustering results give the following important implications: (1) there are three major clusters and (2) stellar metallicity does not play a key role in exoplanet migration.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 42, May 2016, Pages 290-313
نویسندگان
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