Fuzzy clustering decision tree for classifying working wafers of ion implanter

In this paper, we propose a fuzzy clustering decision tree (FCDT) for the classification problem with large number of classes and continuous attributes. A hierarchical clustering concept is introduced to achieve a finer fuzzy partition. The proposed clustering algorithm split the data set into leaf clusters using splitting attributes based on a separation matrix and fuzzy rules. The leaf clusters consisting of the data of more than one class will be further classified using the C4.5. We have successfully applied the FCDT to the classification problem of the working wafers in an ion implanter, and compared the classification results and the computation time with the existing software See5 and CART.