Abstract—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.
Index Terms—Elliptic Fourier Analysis, elliptic fourier
descriptor, female breast clustering, clustering validation, PCA.
Haoyang Xie and Zhicai Yu are with Donghua University, Shanghai,
201620 China (e-mail: xie_haoyang@qq.com, 1039598561@qq.com).
Xi Chen is with SIAS University, Xinzheng, Henan, 451150 China
(e-mail: 56908112@qq.com).
Yueqi Zhong is with the Key Lab of Textile Science and Technology,
Ministry of Education, and Donghua University, Shanghai (e-mail:
zhyq@dhu.edu.cn).
Kai Lu is with Xuchang University, Xuchang, Henan, 461000, China
(e-mail: lukai0373@foxmail.com).
Cite: Haoyang Xie, Xi Chen, Zhicai Yu, Yueqi Zhong, and Kai Lu, "In-depth Analysis of Unsupervised Clustering for Female Breast Shape," International Journal of Machine Learning and Computing vol. 12, no. 1, pp. 37-42, 2022.
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).