There are many possible different semantic relationships between nominals. Classification of such relationships is an important and difficult task (for example, the well known noun compound classification task is a special case of this problem). We propose a novel pattern clusters method for nominal relationship (NR) classification. Pattern clusters are discovered in a large corpus independently of any particular training set, in an unsupervised manner. Each of the extracted clusters corresponds to some unspecified semantic relationship. The pattern clusters are then used to construct features for training and classification of specific inter-nominal relationships. Our NR classification evaluation strictly follows the ACL SemEval-07 Task 4 datasets and protocol, obtaining an f-score of 70.6, as opposed to 64.8 of the best previous work that did not use the manually provided WordNet sense disambiguation tags.