A Kernel Enabled RPCL Algorithm

paper presents a novel Kernel enabled Rival Penalised Competitive Learning (KRPCL) algorithm for clustering. Not only is it able to perform correct clustering without re- striction on cluster shape, but also can automatically find the number of clusters. This algorithm generalizes the Rival Penalised Competitive Learning (RPCL)algorithm (15) by using the state-of-the-art kernel trick (2). Moreover, under the framework of KRPCL, better seeds initialization can be achieved by using spectral analysis on the kernels used in KRPCL. As a third contribution, we show the RPCCL (16), proposed for handling the de-learning rate problem arising in RPCL, can also be generically kernel-enabled, which in turn motivates us to find more ways for specifying the de- learning rate in KRPCL. The experimental results show su- periority of KRPCL in both synthetical datasets and various real data collected from the UCI repository and the Internet newsgroups.