Unsupervised consensus analysis for on-line review and questionnaire data

We run a series of simulation experiments using generated data with random error. We test both the single cluster and multiple cluster models. These experiments show that CONSCLUS is able to recover aggregate rating values and latent cluster assignments better than a range of other aggregation methods. The performance increase over the other aggregation methods is particularly strong when the users have varying competencies. We give an illustrative example using the Movielens dataset. We give a set of recommendations for the practical implementation of CONSCLUS on real world data and show how the user competencies can be used to gain insight into these data that cannot be gained from simple partitioning clustering.