In the paper, the hybrid clustering-classification neural network is proposed. This network allows to increase a quality of information processing under the condition of overlapping classes due to the rational choice of learning rate parameter and introducing special procedure of fuzzy reasoning in the clustering-classification process, which occurs both with external learning signal ("supervised"), and without one ("unsupervised"). As similarity measure neighborhood function or membership one, cosine structures are used, which allow to provide a high flexibility due to self-learning-learning process and to provide some new useful properties. Many realized experiments have confirmed the efficiency of proposed hybrid clustering-classification neural network; also, this network was used for solving diagnostics task of reactive arthritis.Index Terms-hybrid clustering-classification neural network, supervised/unsupervised learning, overlapping classes, diagnostics, reactive arthritis.
I. INTRODUCTIONSelf-organizing maps (SOM) and neural networks of learning vector quantization (LVQ) have seen extensive use for solving different problems in Data Mining domain (clustering, classification, fault detection and compression of information etc.). This type of neural networks was proposed by T. Kohonen [1,2] and represents, in fact, a single-layer feedforward architecture, which provides an operator for mapping of input space into the output space. Operation-wise SOM and LVQ are quite similar to each neuron is fed input signal (sample) producing output, which is used during competition stage to determine winning neuron -usually the one with maximum output signal value. Vector of synaptic weights for winning neuron is the one closest to the input sample in terms of the metric chosen (which is Euclidian metric in most cases). Next is neurons adjustment phase. Synaptic weights of the winning neuron gets moved closer to input sample. Alternatively, a subset of neurons (rather than a single one) can be adjusted -those determined to be "reasonably close" to the input sample are updated. Resulting network output, however, is determined exclusively by winning neuron (this principle is usually referred to as "Winner-Takes-All" (WTA)). It is this principle (WTA) which negatively affects accuracy in case when there are overlapping clusters in underlying data.Taking into account the above mentioned properties of SOM and LVQ networks, it makes sense to introduce fuzzy classification capabilities on top of them, while preserving online operation. In [8,9] fuzzy self-organizing map was proposed, in which conventional neurons are replaced by fuzzy rules. This neural network shows enough high efficiency, but its learning properties were significantly lost especially in on-line mode. In [5,10,11] fuzzy clustering Kohonen network and fuzzy linear vector quantization network are described. In fact, such networks are neural representation of fuzzy c-means (FCM) [3], which is far enough from SOM and LVQ mathematical tool and designed for operatio...
