Improving rival penalized competitive learning using density‐evaluated mechanism

Rival penalized competitive learning (RPCL) and its variants have provided attractive ways to perform clustering without knowing the exact cluster number. However, they are always accompanied by problems of falling in local optima and slow learning speed. Thus we investigate the RPCL and propose a mechanism to directly prune the RPCL's structure by evaluating the data density of each unit. We call the new strategy density‐evaluated RPCL (DERPCL). The communication channel state is estimated by the DERPCL in the simulations, and comprehensive comparisons are made with other RPCLs. Results show that the DERPCL is superior in terms of convergence accuracy and speed.