A Multi-objective Clustering Ensemble Algorithm with Automatic k-Determination
Hang Dai, Weiguo Sheng · 2019
Evolutionary algorithm has been widely employed for data clustering, resulting various evolutionary clustering methods. For a given clustering task, these methods generally deliver different clustering solutions and there is no clear indication which one is the best. Clustering ensemble, which tries to encompass all information contained in different clustering solutions given by different methods, is considered to be an effective approach of improving the robust of solution. However, existing clustering ensemble method generally requires a cluster number as parameter to be specified in advance. Further, they could have the difficulty to preserve the diversity of candidate clustering solutions, which plays an important role in the success of these methods. Considering these two aspects, we propose a multi-objective clustering ensemble algorithm with automatic k-determination. In the proposed method, a modified version of Dual-Similarity Clustering Ensemble (MDSCE), which does not require the cluster number in advance, is developed and employed as crossover operator to generate new clustering partitions during optimizing process. Further, a k-means based process employing the cluster number from MDSCE is employed to generate diverse and high-quality individuals. For evaluation purpose, we have tested our algorithm on a series of real datasets. The results show that the proposed algorithm is able to deliver high-quality clustering solutions and outperforms related clustering algorithms.