Robust Linear Clustering of Mixed Databases Using Alternative c-Means Criterion and Optimal Scaling

Abstract

Alternative c-Means criterion has been proved to be useful for weakening the influences of noise in FCM-type clustering, and was successfully applied to linear clustering. In this paper, alternative c-means criterion was introduced into linear clustering models in conjunction with optimal scaling of category observations with the goal of extracting robust linear substructures from noisy data.