In our case , the clustering is performed by the program Snob , which implements mixture modeling combined with model selection based on the Minimum Message Length ( MML ) criterion ( Wallace and Boulton 1968 ; Wallace 2005 ) .
statistical and inductive inference by minimum message length
C S Wallace
The idea behind the Doc-Pred method is similar to Bickel and Scheffer 's ( 2004 ) : Response documents are grouped into clusters , one of these clusters is predicted for a new request on the basis of the request 's features , and the response that is most representative of the predicted cluster ( closest to the centroid ) is selected . In our case , the clustering is performed by the program Snob , which implements mixture modeling combined with model selection based on the Minimum Message Length ( MML ) criterion ( Wallace and Boulton 1968 ; Wallace 2005 ) . We chose this program because the number of clusters does not have to be specified in advance , and it returns a probabilistic interpretation for its clusters ( this interpretation is used by the Sent-Pred method , Section 3.2.2 ) .
In our case , the clustering is performed by the program Snob , which implements mixture modeling combined with model selection based on the Minimum Message Length ( MML ) criterion ( Wallace and Boulton 1968 ; @@CITATION ) .