Consensus Clustering by Weight Optimization of Input Partitions

This paper proposes a weighted consensus approach for data clustering, where each input basic clustering method is weighted. The weights are automatically determined by solving an optimization problem. Experiments are carried out on three datasets: NSL-KDD, Forest Cover Type, and Phone Accelerometer datasets. The results show the effectiveness of the proposed approach to Big data clustering compared to single clustering methods.