Many of the most effective compression methods involve complicated models. Unfortunately, as model complexity increases, so does the cost of storing the model itself. This paper examines a method to reduce the amount of storage needed to represent a Markov model with an extended alphabet, by applying a clustering scheme that brings together similar states. Experiments run on a variety of large natural language texts show that much of the overhead of storing the model can be saved at the cost of a very small loss of compression efficiency.
Bibliographical noteFunding Information:
* The work of the first author (AB) was supported, in part, by NSF Grant IRI-9307895-A01. The author gratefully acknowledges this support. We also wish to acknowledge support given by the Academy of Finland to TR, t To whom all correspondence should be addressed: tel: (773) 702-8268, fax: (773) 702-9861, firstname.lastname@example.org; email@example.com, and firstname.lastname@example.org