Knowledge discovery in rubber extrusion processes

This paper describes the outcomes of a study that the EDMANS(**) group has recently performed in a rubber extrusion process, focusing on the knowledge discovery phase previous to the system modeling. Some of the tools developed to satisfy the special needs of such a process are also presented: the CiTree algorithm for clustering subpopulations in massive databases and the PAELLA algorithm for outlier detection and data cleaning in non normal samples like those typically found in industrial processes. Finally, the results obtained by these data mining techniques when applied to a real rubber extrusion databases are shown. Key-Words: CiTree, cluster analysis, PAELLA, outlier detection, rubber, extrusion