Multi-source clustering is common data mining task the aim of which is to use several clustering algorithms to analyze different aspects of the same data. Well known applications of multi-source clustering include horizontal collaborative clustering and multi-view clustering, where several algorithms combine their strengths by exchanging information about their finding on local structures with a goal of mutual improvement. However, many of these proposed algorithms and statistical models lack the capability to detect weak collaborations that may prove detrimental to the global clustering process. In this article, we propose a weighing optimization method that will help detecting which algorithms should exchange their information based on the diversity between the different algorithms’ solutions.