  Statistical language models frequently suffer from a lack of training data.
This problem can be alleviated by clustering, because it reduces the number of
free parameters that need to be trained. However, clustered models have the
following drawback: if there is ``enough'' data to train an unclustered model,
then the clustered variant may perform worse. On currently used language
modeling corpora, e.g. the Wall Street Journal corpus, how do the performances
of a clustered and an unclustered model compare? While trying to address this
question, we develop the following two ideas. First, to get a clustering
algorithm with potentially high performance, an existing algorithm is extended
to deal with higher order N-grams. Second, to make it possible to cluster large
amounts of training data more efficiently, a heuristic to speed up the
algorithm is presented. The resulting clustering algorithm can be used to
cluster trigrams on the Wall Street Journal corpus and the language models it
produces can compete with existing back-off models. Especially when there is
only little training data available, the clustered models clearly outperform
the back-off models.
