Abstract：Competitive learning is an important approach for clustering analysis. The rival penalized competitive learning (RPCL) algorithm has the ability of selecting the correct number of clusters automatically, but its performance is sensitive to the selection of learning rate and de-learning rate. In fact, it is unreasonable that all the rival units are treated as redundant units to be penalized in the variant algorithm called rival penalization controlled competitive learning (RPCCL). In this paper, a discriminative rival penalization controlled competitive learning (DRPCCL) is presented. The learning rate of winningunits adaptively adjusts during iteration in the proposed method. Meanwhile, a discriminative penalization controlled mechanism is used to discriminate the redundant units and the correct units in the rival units. The correct units and redundant units are given a slight penalization and a heavier penalization respectively, which makes this algorithm get exact number of clusters and reasonable centre of clusters. The experimental result demonstrates that compared with RPCL and RPCCL, DRPCCL achieves more accurate performance.
2014, Vol. 27 
