1. A computer-implemented method of clustering items, each item having at least one associated feature, the method comprising: storing a data structure in memory the data structure holding a plurality of clusters; for each item, one or more associated features; for each cluster, at least one cluster membership parameter related to a prior probability distribution representing belief about whether any one of the items is a member of that cluster; for each cluster and feature combination, at least one feature parameter related to a prior probability distribution representing belief about whether any one of the items in that cluster is associated with that feature; receiving and storing an input comprising an observed item having observed associated features; updating the parameters in the data structure on a basis of the received input and using a Bayesian update process; identifying features that have a similar feature parameter across all clusters and using a same default value for those feature parameters; and iterating the receiving and updating for a plurality of such inputs.