Hybrid Cluster Analysis Method Based on GA and FCM for Automatically Identifying Joint Sets

A hybrid cluster analysis method based on genetic algorithm (GA) and fuzzy C-means (FCM) algorithm is introduced for the automatic identification of joint sets. The initial cluster centers for FCM are obtained by GA, and then the optimal cluster results can be calculated by FCM on the basis of the work in the first stage. This method eliminates the local optimality disadvantage of FCM and the subjectivity of traditional methods such as pole and contour plots for classifying joints into sets and resolves the conflict between search speed and cluster precision by general GA. The analysis steps, parameters selection, cluster validity and dominant direction determination for identification of joints sets using the hybrid cluster analysis method are discussed based on joint survey data sets.