Mapping-based Adaptively Weighted Ensemble Clustering

Ensemble clustering fuses multiple base clusterings into a partition that is more accurate and more robust than any single one. Existing methods, however, fix the weights before the fusion and apply them at a single granularity, split representation learning and partition solving into two stages, are prone to trivial solutions, and rely on the expensive $n\times n$ co-association matrix. We therefore propose Mapping-based Adaptively Weighted Ensemble Clustering (MAWEC). A continuous weight rates each base clustering and a mapping matrix merges each base cluster into a target cluster, so the two granularities are optimized jointly. Consistency learning, structured learning and weight learning are coupled through one soft label matrix, whose row-wise maximum gives the labels directly. A structural regularizer promotes row sparsity and column balance, and a weight regularizer keeps the weights from collapsing, so three trivial solutions are excluded. Since the consensus is carried by the $n\times m$ association matrix, the fusion is linear in the number of samples. Experiments on eight real-world datasets show that MAWEC attains the highest ACC on all of them and the shortest running time.