Comparison between Fuzzy Kernel C-Means and Sparse Learning Fuzzy C-Means for Breast Cancer Clustering

Ajeng Leudityara Fijri, Zuherman Rustam
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引用次数: 7

Abstract

One of cancers which causes of death among woman in worldwide is breast cancer. It can detected by screening in breast and routine blood analysis. Thats important to assure a greater treatment to reduce the cells of cancer. In this paper, experiments was carried out using Coimbra breast cancer dataset to classify the breast cancer as healthy controls and patients. We used sparse learning fuzzy c-means (SLFCM) clustering method and fuzzy kernel c-means (FKCM) for the compare method. The result of SLFCM give higher accuracy diagnostic than FKCM but, SLFCM need more time to get accuracy results than FKCM.
模糊核c -均值与稀疏学习模糊c -均值在乳腺癌聚类中的比较
导致全世界妇女死亡的癌症之一是乳腺癌。它可以通过乳腺筛查和常规血液分析检测到。这对于确保更有效的治疗来减少癌细胞是很重要的。本文利用科英布拉乳腺癌数据集进行实验,将乳腺癌分为健康对照和患者。采用稀疏学习模糊c均值(SLFCM)聚类方法和模糊核c均值(FKCM)聚类方法进行比较。SLFCM比FKCM具有更高的诊断精度,但SLFCM比FKCM需要更长的时间来获得准确的诊断结果。
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