A New Supervised Clustering Algorithm Based on Min-Max Modular Network with Gaussian-Zero-Crossing Functions

In this paper, we show that the shape, size and location of the receptive field around each instance are different and decided by the distribution of training data in a min-max modular network with Gaussian-zero-crossing functions. Based on this property, we propose a new supervised clustering algorithm which has the following features: First, the incremental clustering ability, which means the number of clusters need not to be predefined, it can grow up automatically, also, the training data need not to be processed iteratively; Second, attaching more importance to border instances than non-border instances, which guarantees the good generalization performance and training data reduction ratio; Third, outlier removal ability, which removes noise instances from training data; Last, cluster combination ability, which reduces the number of clusters further. Experiments on an artificial problem and several real-world applications demonstrate these attractive features of our new clustering algorithm.