Adaptive Kernel Data Streams Clustering Based on Neural Networks Ensembles in Conditions of Uncertainty About Amount and Shapes of Clusters

摘要

The neural network's approach for data stream clustering task, that in online mode are fed to processing in assumption of uncertainty about amount and shapes of clusters, is proposed in the paper. The main idea of this approach is based on the kernel clustering and idea of neural networks ensembles, that consist of the T. Kohonen's self-organizing maps. Each of the clustering neural networks consists of different number of neurons, where number of clusters is connected with the quality of these neurons. All ensemble members process information that sequentially is fed to the system in the parallel mode. Experimental results have proven the fact that the system under consideration could be used to solve a wide range of Data Mining tasks when data sets are processed in an online mode.