In-depth Analysis of Unsupervised Clustering for Female Breast Shape
International Journal of Machine Learning and Computing · 2022
Female breast shape is significantly essential for female healthcare, bra design, etc.However, there is no authoritative standard for breast shape classification.In this paper, we analysis the female breast category by unsupervised clustering the horizontal female breast contours.Specifically, the Elliptic Fourier Descriptors (EFDs), extracted from breast contour, are employed as the contour features.Subsequently, we use PCA to reduce the feature into lower dimensions.Experiments demonstrate that the lower dimensions are enough to present the original features.Then, we employ two widely used clustering algorithms, K-Means++ and FCM, to cluster the female breast contours, and deeply analyze and compare the results of two clustering results in terms of effectiveness.Experimental results demonstrate that the K-Means++ is more suitable for female breast contour clustering, and the results are more reasonable than FCM.