4. The method according to claim 1 , further comprising: receiving a search query; narrowing down a set of pieces of document data by the search query to create a narrowed set of pieces of document data; approximating the narrowed set with a linear sum of document clusters; and inferring, from at least one frequency distribution of a keyword for each document cluster, at least one frequency distribution of the keyword in the narrowed set of pieces of document data.
4. The method according to claim 1 , further comprising: receiving a search query; narrowing down a set of pieces of document data by the search query to create a narrowed set of pieces of document data; approximating the narrowed set with a linear sum of document clusters; and inferring, from at least one frequency distribution of a keyword for each document cluster, at least one frequency distribution of the keyword in the narrowed set of pieces of document data. 5. The method according to claim 4 , wherein approximating the narrowed set is performed according to an equation: f ⁡ ( ω ) q , k = ∑ c ⁢ a c ⁢ f ⁡ ( ω ) c , k wherein f(ω) q,k is a frequency coefficient of a time series of frequencies of occurrence of pieces of document data d containing a keyword k in results of a search query q, and a c is acquired according to equations 3 and 4 described below: D q = ∑ c ⁢ a c ⁢ D c a c = 〈 D q ❘ D c 〉 where D q denotes a narrowed set of pieces of document data expressed as a vector in a space in which each piece of document data is set as a dimension, D c is a vector in a case where, in the same space, a document cluster is expressed with a component of each dimension being a score of a document corresponding to the dimension in the document cluster, and a c is an inner product of D q and D c .