Consensus clustering aims to fuse several existing basic partitions into an integrated one;this has been widely recognized as a promising tool for multi-source and heterogeneous data clustering.Owing to robust and high-quality performance over traditional clustering methods,consensus clustering attracts much attention,and much efforts have been devoted to develop this field.In the literature,the K-means-based Consensus Clustering (KCC) transforms the consensus clustering problem into a classical K-means clustering with theoretical supports and shows the advantages over the state-of-the-art methods.Although KCC inherits the merits from K-means,it suffers from the initialization sensitivity.Moreover,the current consensus clustering framework separates the basic partition generation and fusion into two disconnected parts.To solve the above two challenges,a novel clustering algorithm,named Greedy optimization of K-means-based Consensus Clustering (GKCC) is proposed.Inspired by the well-known greedy K-means that aims to solve the sensitivity of K-means initialization,GKCC seamlessly combines greedy K-means and KCC together,achieves the merits inherited by GKCC and overcomes the drawbacks of the precursors.Moreover,a 59-sampling strategy is conducted to provide high-quality basic partitions and accelerate the algorithmic speed.Extensive experiments on 36 benchmark datasets demonstrate the significant advantages of GKCC over KCC and KCC++ in terms of the objective function values and standard deviations and external cluster validity.
展开▼
