Unsupervised algorithms for the automatic classification of EWS maps: a comparison

Recently, it has been shown that the classification of electrical wafer sorting failure maps can be performed by means of unsupervised methods. In this work four different unsupervised methods are compared: SOM, K-means, neural gas, and an expectation maximization. The algorithms are compared using a benchmark based on a probabilistic model. The performance of the classification is assessed by means of an new index, called index-F, based on the knowledge of the real classification. Moreover it is studied the correlation between the proposed index and the following indexes: CH-index, D-index, I-index and average likelihood.