Hierarchical fuzzy clustering decision tree for classifying recipes of ion implanter

Abstract

In this paper, we propose a hierarchical fuzzy clustering decision tree (HFCDT) for the classification problem with large number of classes and continuous attributes. The HFCDT combines a division-degree matrix based hierarchal clustering technique with the entropy-based C4.5 decision tree algorithm. A hierarchical clustering concept is introduced to achieve a finer fuzzy partition. The hierarchical clustering technique splits the data set into leaf clusters using splitting attributes based on a division-degree matrix and fuzzy rules. The leaf clusters consisting of the data of more than one class will be further classified using the C4.5 algorithm. We have successfully applied the HFCDT for classifying recipes of the working wafers in an ion implanter, and compared the classification results and the training time with the existing software See5 and CART. The comparison results show that the HFCDT not only performs better than See5 and CART in the aspect of 10-fold cross validation for the average of total classification error rates but also consumes less training time. Thus, HFCDT obtains a very successful classification result. This also demonstrates why the hierarchical clustering technique helps reduce the computational complexity of the C4.5 algorithm.