author = "Carruba, V. and Aljbaae, Safwan and Lucchini, A.",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Universidade Estadual Paulista 
                title = "Machine-learning identification of asteroid groups",
              journal = "Monthly Notices of the Royal Astronomical Society",
                 year = "2019",
               volume = "488",
               number = "1",
                pages = "1377--1386",
                month = "Sept.",
             keywords = "methods: data analysis, celestial mechanics, minor planets, 
                         asteroids: general.",
             abstract = "Asteroid families are groups of asteroids that share a common 
                         origin. They can be the outcome of a collision or be the result of 
                         the rotational failure of a parent body or its satellites. 
                         Collisional asteroid families have been identified for several 
                         decades using hierarchical clustering methods (HCMs) in proper 
                         elements domains. In this method, the distance of an asteroid from 
                         a reference body is computed, and, if it is less than a critical 
                         value, the asteroid is added to the family list. The process is 
                         then repeated with the new object as a reference, until no new 
                         family members are found. Recently, new machine-learning 
                         clustering algorithms have been introduced for the purpose of 
                         cluster classification. Here, we apply supervised-learning 
                         hierarchical clustering algorithms for the purpose of asteroid 
                         families identification. The accuracy, precision, and recall 
                         values of results obtained with the new method, when compared with 
                         classical HCM, show that this approach is able to found family 
                         members with an accuracy above 89.5 per cent, and that all 
                         asteroid previously identified as family members by traditional 
                         methods are consistently retrieved. Values of the areas under the 
                         curve coefficients below Receiver Operating Characteristic curves 
                         are also optimal, with values consistently above 85 per cent. 
                         Overall, we identify 6 new families and 13 new clumps in regions 
                         where the method can be applied that appear to be consistent and 
                         homogeneous in terms of physical and taxonomic properties. 
                         Machine-learning clustering algorithms can, therefore, be very 
                         efficient and fast tools for the problem of asteroid family 
                  doi = "10.1093/mnras/stz1795",
                  url = "http://dx.doi.org/10.1093/mnras/stz1795",
                 issn = "0035-8711 and 1365-2966",
             language = "en",
        urlaccessdate = "19 abr. 2021"