1. A computer-implemented method of determining cluster attractors for use in clustering a plurality of documents, each document comprising at least one term, each term comprising one or more words, the method comprising: causing a computer to calculate, in respect of each term, a probability distribution that is indicative of in the instance where a document comprises said term and said one other term that co-occurs with said term in at least one of said documents, the frequency of occurrence of said one other term, and in the instance where a document comprises said term and more than one other term that co-occurs with said term in at least one of said documents, the respective frequency of occurrence of each other term, that co-occurs with said term in at least one of said documents; causing a computer to calculate, in respect of each term, the entropy of the respective probability distribution; and causing the computer to select at least one of said probability distributions as a cluster attractor depending on the respective entropy value; wherein the selected cluster attractor is a clustering focus for at least some of said documents, and wherein said probability distribution is calculated as p ⁡ ( y ❘ z ) = ⁢ ∑ x ⁢ ∈ ⁢ X ⁡ ( z ) ⁢ ⁢ tf ⁡ ( x , y ) ∑ x ⁢ ∈ ⁢ X ⁡ ( z ) , t ⁢ ∈ ⁢ Y ⁢ tf ⁡ ( x , t ) where tf(x, y) is a term frequency of a term y in a document x and X(z) is a set of all documents of said plurality of documents that contain a term z and where t is a term index.