A Clustering Algorithm for Tunnel Boring Machine Data Based on Ridge Regression and Fuzzy C-Means

Abstract

Abstract A fuzzy clustering data partitioning method based on ridge regression is proposed to address the high correlation between the data attributes of the tunnel boring machine. The method utilizes the fuzzy partition matrix as the weight for ridge regression analysis and then employs the resulting regression equation as the clustering model to iteratively approach the target minimum value. This process enables data clustering based on the relevant characteristics of the data attributes. Experimental results using functional data sets demonstrate that this method achieves higher accuracy in clustering functional correlation data compared to traditional methods. Additionally, the proposed algorithm is evaluated using measured data from a bid section of a city subway. Results indicate that the algorithm effectively addresses the problem of data classification and key performance index prediction, with a misclassification rate and prediction error of 23.8% and 2.11%, respectively. These findings meet the engineering requirements and provide support for the subsequent data analysis of the tunnel boring machine.