Clustering and classification of cases using learned global feature weights

We propose a method to improve the performance of clustering and classification of cases in a large-scale case-base by using a learned global feature weight methodology. This methodology is based on the idea that we could use similarity measure to find several concepts (clusters) in the problem-domain such that those cases in a cluster are closely related among themselves while among different clusters those cases are farther apart. It was demonstrated in the experiment that the performance of clustering with learned global feature weights is much better than the performance without global feature weights in terms of the retrieval efficiency and accuracy of solution provided by the system