A new image recognition method based on fuzzy rough sets theory is proposed, and its implementation discussed. The performance of this method as applied to ferrography image recognition is evaluated. It is shown that...A new image recognition method based on fuzzy rough sets theory is proposed, and its implementation discussed. The performance of this method as applied to ferrography image recognition is evaluated. It is shown that the new method gives better results than fuzzy or rough sets method when used alone.展开更多
提出基于知识度量的模糊粗糙c-均值聚类(fuzzy rough c-means based on the knowledge measure,KFRCM)算法。传统聚类算法在处理具有模糊边界的数据时存在一定的局限性,表现为对初始聚类中心较为敏感且在高维空间中效率较低。为解决上...提出基于知识度量的模糊粗糙c-均值聚类(fuzzy rough c-means based on the knowledge measure,KFRCM)算法。传统聚类算法在处理具有模糊边界的数据时存在一定的局限性,表现为对初始聚类中心较为敏感且在高维空间中效率较低。为解决上述问题,引入特征加权的知识度量,结合模糊隶属度函数与粗糙集近似算子,采用高斯核相似度以增强边界特性。实验采用14个数据集,实验结果表明,KFRCM算法的聚类准确性、稳定性和计算效率均优于6种主流聚类算法。该研究首次将知识度量与模糊粗糙聚类相结合,为开发更为可靠和适应性更强的聚类算法提供了新的思路和算法。展开更多
文摘A new image recognition method based on fuzzy rough sets theory is proposed, and its implementation discussed. The performance of this method as applied to ferrography image recognition is evaluated. It is shown that the new method gives better results than fuzzy or rough sets method when used alone.
文摘提出基于知识度量的模糊粗糙c-均值聚类(fuzzy rough c-means based on the knowledge measure,KFRCM)算法。传统聚类算法在处理具有模糊边界的数据时存在一定的局限性,表现为对初始聚类中心较为敏感且在高维空间中效率较低。为解决上述问题,引入特征加权的知识度量,结合模糊隶属度函数与粗糙集近似算子,采用高斯核相似度以增强边界特性。实验采用14个数据集,实验结果表明,KFRCM算法的聚类准确性、稳定性和计算效率均优于6种主流聚类算法。该研究首次将知识度量与模糊粗糙聚类相结合,为开发更为可靠和适应性更强的聚类算法提供了新的思路和算法。
