Classification of Breast Cancer using Fast Fuzzy Clustering based on Kernel

9th Annual Basic Science International Conference 2019

自然科学（总论）

Rustam, Zuherman^1 ; Hartini, Sri^1

Department of Mathematics, University of Indonesia, Depok

16424, Indonesia^1

关键词: Accuracy of classifications;    C-means algorithms;    Fixed numbers;    Fuzzy C-means algorithms;    Initial process;    Kernel function;    Life expectancies;    Machine learning repository;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/546/5/052067/pdf
DOI  :  10.1088/1757-899X/546/5/052067

学科分类：自然科学（综合）

来源: IOP

【 摘 要 】

Breast cancer is the second leading cause of death in women in the world. The classification is the initial process of executing patient treatment, which is important as it increases life expectancy as well as quality. In this paper, a new method is proposed based on kernel, which is modified from KC-Means: it combines K-Means, Fuzzy C-Means algorithm, and kernel function. The C-Means algorithm is applied on the centers of a fixed number of groups founded by K-Means, and the kernel function is expected to improve the accuracy of classification with its ability to separate data which cannot be separated linearly. We applied the proposed method on a dataset of 201 breast cancer and 85 non-breast cancer samples from the UC Irvine Machine Learning Repository. Results concluded that fast fuzzy clustering has an accuracy of 85.26%, but fast fuzzy clustering based on kernel is 89.74%, with a better running time on average than 90.95% with the same method.