Implementation and Comparison of Eye Diagram Measurements Based on Clustering Algorithms

In this paper, the eye diagram parameters are measured and calculated by clustering algorithms. The eye diagram results are used as a dataset, and a comparison of the measurement results is carried out to verify the feasibility of the measurement method. Fuzzy C-Means (FCM) is robust to noise and outliers in data. It is chosen as an improved scheme of K-Means, which is more effective in dealing with fuzzy datasets. It has an advantage in terms of computational speed without reducing the accuracy of parameter extraction.