Improving cluster analysis by co-initializations

Abstract Many modern clustering methods employ a non-convex objective function and use iterative optimization algorithms to find local minima. Thus initialization of the algorithms is very important. Conventionally the starting guess of the iterations is randomly chosen; however, such a simple initialization often leads to poor clusterings. Here we propose a new method to improve cluster analysis by combining a set of clustering methods. Different from other aggregation approaches, which seek for consensus partitions, the participating methods in our method are used consequently, providing initializations for each other. We present a hierarchy, from simple to comprehensive, for different levels of such co-initializations. Extensive experimental results on real-world datasets show that a higher level of initialization often leads to better clusterings. Especially, the proposed strategy is more effective for complex clustering objectives such as our recent cluster analysis method by low-rank doubly stochastic matrix decomposition (called DCD). Empirical comparison with three ensemble clustering methods that seek consensus clusters confirms the superiority of improved DCD using co-initialization.