Clustering Using the Contrast Model
An algorithm is developed for generating featural representations from similarity data using Tversky’s (1977) Contrast Model. Unlike previous additive clustering approaches, the algorithm fits a representational model that allows for stimulus similarity to be measured in terms of both common and distinctive features. The important issue of striking an appropriate balance between data fit and representational complexity is addressed through the use of the Geometric Complexity Criterion to guide model selection. The ability of the algorithm to recover known featural representations from noisy data is tested, and it is also applied to real data measuring the similarity of