Class decomposition via clustering: a new framework for low-variance classifiers

We propose a preprocessing step to classification that applies a clustering algorithm to the training set to discover local patterns in the attribute or input space. We demonstrate how this knowledge can be exploited to enhance the predictive accuracy of simple classifiers. Our focus is mainly on classifiers characterized by high bias but low variance (e.g., linear classifiers); these classifiers experience difficulty in delineating class boundaries over the input space when a class distributes in complex ways. Decomposing classes into clusters makes the new class distribution easier to approximate and provides a viable way to reduce bias while limiting the growth in variance. Experimental results on real-world domains show an advantage in predictive accuracy when clustering is used as a preprocessing step to classification.