This work proposes a novel, classificatory analysis based relevance feedback framework based on a user-centric model of information need that is independent of any particular retrieval paradigm. The model of the user need is based on the principle that a complete representation of the user need is contained in an exhaustive user classification of the collection. This model provides a conceptually appealing basis for relevance feedback; each successive iteration of relevance feedback can be treated as a classification that becomes a closer approximation of the user's information need. The system iteratively achieves a better understanding of the user's information need, gradually converging to a satisfactory set of results. The framework is based on Rough Set Theory, which is explicitly designed to deal with classificatory analysis incorporating uncertainty and approximation.