15. A computer-implemented method of identifying additional keywords to suggest to an advertiser on a basis of an advertisement having a plurality of subscribed keywords, the method comprising: receiving an advertisement having a plurality of subscribed keywords; accessing a clustering system comprising a data structure holding a probabilistic cluster model having been trained on a data set comprising a plurality of advertisements having subscribed keywords and that cluster model being arranged to model uncertainty in the data set; using the clustering system to generate keywords and associated probabilities on a basis of the received advertisement; identifying features that have a similar feature parameter across all clusters and using a same default value for those feature parameters; and outputting the generated keywords as suggestions to the advertiser.
15. A computer-implemented method of identifying additional keywords to suggest to an advertiser on a basis of an advertisement having a plurality of subscribed keywords, the method comprising: receiving an advertisement having a plurality of subscribed keywords; accessing a clustering system comprising a data structure holding a probabilistic cluster model having been trained on a data set comprising a plurality of advertisements having subscribed keywords and that cluster model being arranged to model uncertainty in the data set; using the clustering system to generate keywords and associated probabilities on a basis of the received advertisement; identifying features that have a similar feature parameter across all clusters and using a same default value for those feature parameters; and outputting the generated keywords as suggestions to the advertiser. 16. A method as claimed in claim 15 wherein an output comprises a list of at least some of the generated keywords ranked using estimates of the associated probabilities.