Kolay, NurdanErdoğmuş, Pakize2020-04-302020-04-302016978-1-5090-0876-6https://hdl.handle.net/20.500.12684/4792Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT) -- APR 26-27, 2016 -- Istanbul, TURKEYWOS: 000387072000008In this study, it is aimed to classify breast cancer data attained from UCI(University of California-Irvine), Machine Learning Laboratory with some Machine Learning Techniques. With this aim, clustering performance of some distance measures in Matlab(C) has been compared, using breast cancer data. Later without using any pre-processing, some of the machine learning techniques are used for the clustering breast cancer data, using WEKA data mining software(C). As a result, it has been seen that distance measures effects the clustering performance nearly 12 percentage and the succes of the classification varies from % 45 to % 79, according to the methods.trinfo:eu-repo/semantics/closedAccessMachine LearningBreast CancerK-means clusteringFuzzy C-meansSupport Vector MachinesConference ObjectN/A