The Evidence Accumulation Clustering, EAC, algorithm is a clustering ensemble method which uses co-occurrence statistics to derive a similarity matrix, referred to as co-association matrix. In order to obtain a final consensus clustering the co-association matrix is fed to a pairwise similarity clustering algorithm. The method has thus O(n^2) space complexity, which can constitute a relevant bottleneck to its scalability. In this paper we propose a new formulation which works using a partial set of the co-occurrences, greatly reducing the computational time and space, leading to a scalable algorithm. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach.