Cluster-based adaptive metric classification
Introducing adaptive metric has been shown to improve the results of distance-based classification algorithms. Existing methods are often computationally intensive, either in the training or in the classification phase. We present a novel algorithm that we call Cluster-Based Adaptive Metric (CLAM) classification. It first determines the number of clusters in each class of a training set and then computes the parameters of a Mahalanobis distance for each cluster. The derived Mahalanobis distances are then used to estimate the probability of cluster- and, subsequently, class-membership. We compare the proposed algorithm with other classification algorithms using 10 different data sets. The proposed CLAM algorithm is as effective as other adaptive metric classification algorithms yet it is simpler to use and in many cases computationally more efficient.
adaptive metric cluster estimation gap statistic prototype-based classification principal component analysis bayes' rule