Earticle

In this paper, we present a sequence analysis method, which is one of the advanced data mining techniques, to identify and extract unique patterns from wafer manufacturing data. Wafer fabrication in the semiconductor industry is one of the most complex manufacturing processes. For such highly complicated operations, maintaining high yields through the statistical process control as a sole monitoring method for quality control is obviously inefficient. We thus investigate the intelligent and semi-automatic technique to help industrial engineers analyzing their production data. Our proposed method has the ability to induce patterns that can reveal and differentiate low performance processes from the normal ones. We also provide program coding of the proposed sequence analysis method, implemented with the R language, for easy experimental repetition.

Recently, there has been enormous growth in the amount of commercial and scientific data, such as protein sequences, retail transactions, and web-logs. Such datasets consist of sequence data that have an inherent sequential nature. However, only a few of the existing clustering algorithms consider sequentiality. This study presents a method for clustering such sequence datasets. The similarity between sequences must be decided before clustering the sequences. This study proposes a new similarity measure to compute the similarity between two sequences using a sequence element. Two clustering algorithms using the proposed similarity measure are proposed: a hierarchical clustering algorithm and a scalable clustering algorithm that uses sampling and a k-nearest neighbor method. Using a splice dataset and synthetic datasets, we show that the quality of clusters generated by our proposed clustering algorithms is better than that of clusters produced by traditional clustering algorithms.