Many outlier detection methods identify outliers ignoring any structure in data. However, it is sometimes beneficial to integrate outlierness and a method that groups data, such as clustering. This enhances both outlier and cluster analysis. In this paper, a fuzzy approach is proposed for integrating results from an outlier detection method and a clustering algorithm. A universal set of clusters is proposed which combines clusters obtained from clustering, and a virtual cluster for the outliers. The approach has two phases; the first computes patterns' initial memberships for the outlier cluster, and the second calculates memberships for the universal clusters, using an iterative membership propagation technique. The proposed approach is general and can combine any outlier detection method with any clustering algorithm. Both low and high dimensional data sets are used to illustrate the impact of the proposed approach.