Abstract :
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.
Keywords :
generalisation (artificial intelligence); learning (artificial intelligence); minimax techniques; pattern clustering; Gaussian-zero-crossing functions; incremental clustering ability; min-max modular network; supervised clustering algorithm; Clustering algorithms; Computer science; Data engineering; Gaussian distribution; Gaussian processes; Iterative algorithms; Joining processes; Noise reduction; Shape; Training data;