A new similarity combining reconstruction coefficient with pairwise distance for agglomerative clustering

Agglomerative clustering is a mainstream clustering method that can produce an informative hierarchical structure of clusters. Existing similarities in agglomerative clustering are typically based on the pairwise distance. Although this type of similarity captures the local structure of data well, it is sensitive to noise and outliers because it considers only the distance between data points. In this paper, we propose a new similarity called RCPD by combining the reconstruction coefficient, which is robust to noise and outliers, with the pairwise distance for agglomerative clustering. Our new similarity takes advantage of both the distance between data points and the linear representation among data points. Thus, RCPD not only captures the local structure of data well but is also robust to noise and outliers. The experimental results on 11 real-world benchmark datasets show that our new clustering method consistently outperforms many state-of-the-art clustering approaches. (C) 2019 Elsevier Inc. All rights reserved.