| Citation: | LI Hong, ZHANG Zhibin. Ensemble clustering algorithm based on rapid simulated annealing[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(8): 1646-1652. doi: 10.13700/j.bh.1001-5965.2018.0647(in Chinese)
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There are two key issues in applying simulated annealing algorithm to solve the problem of ensemble clustering. One is how to use basic partition information in annealing process to obtain better result, and the other is how to accelerate the algorithm convergence. In this paper, the rapid simulated annealing based on voting (BV-RSA) model is presented, in which the complete and partial consensuses of basic partitions are used to recognize super-objects and construct voting box for each super-object. In the process of simulated annealing, some data samples represented by a super-object are controlled to move in a group, and the motion direction of a super-object is selected randomly in the scope of its voting box, thus reducing moving randomness and speeding up the clustering of super-objects. Experiments on multiple data sets demonstrate that the BV-RSA model performs well in both clustering accuracy and robustness.
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