KNOWLEDGE BASED DISCOVERY IN FED-BATCH BIOPROCESS

We present a data mining approach based on a clustering method to detect and characterize states in a fed-batch processes. This method is based on the detection of singularities in biochemical signals and on the correlation between these signals. A segmentation based on maxima of wavelets transform is used to make an adaptive and dynamical correlation of the signals. The segmentation enables the detection of the borders of states whereas the correlation enables to characterize the physiological states. The method is applied successfully on a fed-batch process and particular states (difficult to detect with classical methods of classification) are detected and characterized.