Fuzzy Clustering from Subset-Clustering to Fullset-Membership

Fuzzy theory, which extends precise binary logic to continuous fuzzy logic, provides an effective tool for uncertainty problems in machine learning and thus, evolved fuzzy methods such as fuzzy c-means and fuzzy graph clustering. Among them, graph-based clustering methods have become a hot spot in the field of unsupervised clustering due to their good ability to process nonlinear data. Unfortunately, they usually suffer from high time complexity and cumbersome regularization parameter tuning, so their practical applications are greatly limited. To this end, we propose an efficient graph-cut algorithm called fS2F. Based on the similarity graph between the dataset and landmark subset, fS2F transforms the membership learning of the entire dataset into a clustering problem of representative points, which greatly improves its clustering efficiency. In addition, fS2F softly constrains the cluster size in a way that does not require additional regularization parameters so that it can be widely and conveniently applied. The article also presents the optimization method for this model and demonstrates its effectiveness through experiments.