  The context transformation and generalized context transformation methods, we
introduced recently, were able to reduce zero order entropy by exchanging
digrams, and as a consequence, they were removing mutual information between
consecutive symbols of the input message. These transformations were intended
to be used as a preprocessor for zero-order entropy coding algorithms like
Arithmetic or Huffman coding, since we know, that especially Arithmetic coding
can achieve a compression rate almost of the size of Shannon's entropy.
  This paper introduces a novel algorithm based on the concept of generalized
context transformation, that allows transformation of words longer than simple
digrams. The higher order contexts are exploited using recursive form of a
generalized context transformation. It is shown that the zero order entropy of
transformed data drops significantly, but on the other hand, the overhead given
by a description of individual transformations increases and it has become a
limiting factor in a successful transformation of smaller files.
