Examinando por Materia "K-means clustering"
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Ítem Análisis comparativo entre: «el análisis exploratorio de datos» y los modelos de «árboles de decisión» y «kmeans » en el diagnóstico de la malignidad en algunos exámenes de cáncer de mama. Un estudio de caso(Revista Espacios, 2018-01-01) Sánchez Zuleta, C.C.; Giraldo Marín, L.M.; Piedrahita Escobar, C.C.; Bonet, I.; Lochmüller, C.; Tabares Betancur, M.S.; Peña, A.; Sánchez Zuleta, C.C.; Giraldo Marín, L.M.; Piedrahita Escobar, C.C.; Bonet, I.; Lochmüller, C.; Tabares Betancur, M.S.; Peña, A.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las ComunicacionesThe exponential growth of medical data has generated the need to implement new techniques of information analysis that support decision making. The objective of this article is to identify the aggregated value that data mining models have in decision making in the information given by exploratory analysis. It was used a case study methodology for two data sets, that look to determine the malignity of detected masses, in the breasts of patients, through the interpretation of attributes registered from the mases. The results show a complementary behavior of both techniques. © 2018.Ítem Métodos de machine learning con algoritmos de clúster no supervisados, una alternativa de segmentación de las pymes colombianas para plantear estrategias de acuerdo con sus condiciones económicas(Universidad EAFIT, 2022) Ramírez Mendoza, Durley Yalile; Orozco Echeverry, César AugustoThis research created a new grouping alternative using machine learning tools such as K-means and agglomerative clustering models, based on financial information from 2016 to 2019 of 10,001 Colombian SMEs. From these models twelve clusters originated that have 98.44% of the evaluated data and it was determined that the model that presented the best clustering result was the agglomerative model which generates the following main groups: a first group with negative margins and a debt exceeding 61%, a second group starting with a range between -10% to 40% of its margins and a debt below 60%, and a third group with positive margins and a debt between 11 and 80%. Finally, these groups create strategies according to the economic conditions of each of them.