  In addition to the frequency of terms in a document collection, the
distribution of terms plays an important role in determining the relevance of
documents for a given search query. In this paper, term distribution analysis
using Fourier series expansion as a novel approach for calculating an abstract
representation of term positions in a document corpus is introduced. Based on
this approach, two methods for improving the evaluation of document relevance
are proposed: (a) a function-based ranking optimization representing a user
defined document region, and (b) a query expansion technique based on
overlapping the term distributions in the top-ranked documents. Experimental
results demonstrate the effectiveness of the proposed approach in providing new
possibilities for optimizing the retrieval process.
