Many information retrieval applications have to present their results in the form of ranked lists, in which documents must be sorted in a descending order according to their relevance to a given query. This has led the interest of the information retrieval community in methods that automatically learn effective ranking models, and recently machine learning techniques have also been applied to model construction. Most of the existing methods do not take into consideration the fact that significant homogeneity exists between query-document pairs related to user’s feedback. In this research, a novel method which clusters patterns in the training data with their relevance from the user, and then uses the discovered rules to rank documents at query-time. A systematic evaluation of the proposed method using the LETOR benchmark dataset is posposed. The experimental results show that the proposed method outperforms the state-of-the-art methods with no need of time-consuming and laborious pre-processing.