I. Batyrshin and G. Sidorov (Eds.): MICAI 2011, Part II, LNAI 7095, pp. 153–164, 2011. © Springer-Verlag Berlin Heidelberg 2011 Clustering Ensemble Framework via Ant Colony Hamid Parvin and Akram Beigi Islamic Azad University, Nourabad Mamasani Branch, Nourabad Mamasani, Iran hamidparvin@mamasaniiau.ac.ir, beigi@iust.ac.ir Abstract. 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. Generating a set of primary partitions that are different from each other, and then aggregation the partitions via a consensus function to generate the final partition, is the common policy of ensembles. Another alternative in the ensem- ble 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 en- semble learning based on the ant colony clustering algorithm. Experimental re- sults on some real-world datasets are presented to demonstrate the effectiveness of the proposed method in generating the final partition. Keywords: Ant Colony, Data Fusion, Clustering. 1 Introduction Data clustering is an important technique for statistical data analysis. Machine learn- ing typically regards data clustering as a form of unsupervised learning. The aim of clustering is the classification of similar objects into different cluster, or partitioning of a set of unlabeled objects into homogeneous groups or clusters (Faceli et al., 2006). There are many applications which use clustering techniques to discover structures in data, such as Data Mining (Faceli et al., 2006), pattern recognition, image analysis, and machine learning (Deneubourg et al., 1991). Ant clustering is introduced by Deneubourg et al. (1991). In that model, the swarm intelligence of real ants is inserted into a robot for the object collecting task. Lumer and Faieta (1994) based on how ants organize their food in their nest, added the Euc- lidean distance formula as similarity density function to Deneubourg’s model. Ants in their model had three kinds of abilities: speed, short-term memory, and behavior ex- change. There are two major operations in ant clustering: picking up an object from a clus- ter and dropping it off into another cluster (Tsang and Kwong, 2006). At each step, some ants perform pick-up and drop-off based on some notions of similarity between an object and the clusters. Azimi et al. (2009) define a similarity measure based on the co-association matrix. Their approach is fully decentralized and self-organized and allows clustering structure to emerge automatically from the data.