Ensemble-based learning is a very promising option to reach a robust partition. Due to covering the faults of each other, the classifiers existing in the ensemble can do the classification task jointly more reliable than each of them. There is a straightforward way to generate a set of primary partitions that are different from each other, and then to aggregate the partitions via a consensus function to generate the final partition. Another alternative in the ensemble learning is to turn to fusion of different data from originally different sources. Swarm intelligence is also a new topic where the simple agents work in such a way that a complex behavior can be emerged. Ant colony algorithm is a powerful example of swarm intelligence. In this paper we introduce a new clustering ensemble learning based on the Ant Colony clustering algorithm. Indeed ensemble needs diversity vitally and swarm is inherently involved in randomness. Different runnings of ant colony clustering result in a number of diverse partitions. Considering these results as a new space datasets we employ a final clustering by a simple partitioning algorithm to aggregate them in a consensus partition. Experimental results on some real-world datasets are presented to demonstrate the effectiveness of the proposed method in generating the final partition.

Login Required

Please login to access the full PDF of this article.

🔑 Author Login