An application of minimum description length clustering to partitioning learning curves

We apply a minimum description length-based clustering technique to the problem of partitioning a set of learning curves. The goal is to partition experimental data collected from different sources into groups of sources that are statistically the same. We solve this problem by defining statistical models for the data generating processes, then partitioning them using the normalized maximum likelihood criterion. Unlike many alternative model selection methods, this approach is optimal (in a minimax coding sense) for data of any sample size. We present an application of the method to the cognitive modeling problem of partitioning of human learning curves for different categorization tasks.