Abstract: Ensemble clustering integrates a set of base clustering results to generate a
stronger one. Existing methods usually rely on a co-association (CA) matrix
that measures how many times two samples are grouped into the same cluster
according to the base clusterings to achieve ensemble clustering. However, when
the constructed CA matrix is of low quality, the performance will degrade. In
this paper, we propose a simple yet effective CA matrix self-enhancement
framework that can improve the CA matrix to achieve better clustering
performance. Specifically, we first extract the high-confidence (HC)
information from the base clusterings to form a sparse HC matrix. By
propagating the highly-reliable information of the HC matrix to the CA matrix
and complementing the HC matrix according to the CA matrix simultaneously, the
proposed method generates an enhanced CA matrix for better clustering.
Technically, the proposed model is formulated as a symmetric constrained convex
optimization problem, which is efficiently solved by an alternating iterative
algorithm with convergence and global optimum theoretically guaranteed.
Extensive experimental comparisons with twelve state-of-the-art methods on
eight benchmark datasets substantiate the effectiveness, flexibility and
efficiency of the proposed model in ensemble clustering. The codes and datasets
can be downloaded at https://github.com/Siritao/EC- CMS.