  In this paper, we propose a general model for plane-based clustering. The
general model contains many existing plane-based clustering methods, e.g.,
k-plane clustering (kPC), proximal plane clustering (PPC), twin support vector
clustering (TWSVC) and its extensions. Under this general model, one may obtain
an appropriate clustering method for specific purpose. The general model is a
procedure corresponding to an optimization problem, where the optimization
problem minimizes the total loss of the samples. Thereinto, the loss of a
sample derives from both within-cluster and between-cluster. In theory, the
termination conditions are discussed, and we prove that the general model
terminates in a finite number of steps at a local or weak local optimal point.
Furthermore, based on this general model, we propose a plane-based clustering
method by introducing a new loss function to capture the data distribution
precisely. Experimental results on artificial and public available datasets
verify the effectiveness of the proposed method.
