Data stream mining and soft computing applications

In current industrial systems, the necessity of data stream mining and learning from data streams is increasingly becoming more prevalent and urgent, due to speed, volume and on-line nature of the data generated by such systems. While conventional batch and off-line training approaches provide a possible solution, such approaches are often too time and memory intensive, and cannot process the data at the high enough rate that is often desired. This is true even when batch and off-line approaches are applied to sliding windows or onto streaming samples gathered from reservoir computing techniques.