Cooperative and penalized competitive learning with application to kernel-based clustering

• Propose a novel cooperative and penalized competitive learning (CPCL) model.
• Design the CPCL algorithm featuring high accuracy and performance robustness.
• Introduce the proposed competition mechanism in kernel-based clustering framework.
• Present a new kernel-based learning algorithm without knowing true cluster number.

Competitive learning approaches with individual penalization or cooperation mechanisms have the attractive ability of automatic cluster number selection in unsupervised data clustering. In this paper, we further study these two mechanisms and propose a novel learning algorithm called Cooperative and Penalized Competitive Learning (CPCL), which implements the cooperation and penalization mechanisms simultaneously in a single competitive learning process. The integration of these two different kinds of competition mechanisms enables the CPCL to locate the cluster centers more quickly and be insensitive to the number of seed points and their initial positions. Additionally, to handle nonlinearly separable clusters, we further introduce the proposed competition mechanism into kernel clustering framework. Correspondingly, a new kernel-based competitive learning algorithm which can conduct nonlinear partition without knowing the true cluster number is presented. The promising experimental results on real data sets demonstrate the superiority of the proposed methods.