Generalized Context Transformations -- Enhanced Entropy Reduction

Context transformations is a very simple data transformation method that we presented recently and it is used to decrease uncertainty in input data. The transformation is based on exchange of two different di-grams. This paper is focused on new consequences of the relationships discovered subsequently. We were able to find a mathematical model which predicts the efficiency of each transformation. The new type of the transformation, Generalized context transformation, developed recently is more efficient than the previous one and it is able to remove almost all redundancy based on the symbols mutual information. The newly developed algorithm is computationally and entropic ally more efficient than the previous one.