Clustering Combination Based on Ant Colony Algorithm

摘要

Social insects such as ants can be viewed as distributed systems capable of solving complex problems. The collective behavior of ant colonies and the mechanisms of their self-organization, pheromone communication and task partitioning have inspired researchers to design new algorithms for solving problems in many application fields, e.g. combinatorial optimization, communication networks, robotics. As an unsupervised learning technique, clustering is a division of data into groups of similar objects. The ant-based clustering algorithm has currently applications in the data mining community. This paper presents a new ant-based clustering combination algorithm, which imitates the cooperative behavior of multi-ant colonies. Initially each ant colony takes different types of ant moving speeds to generate independent clustering results, and then these results are combined using a hypergraph. Finally an ant-based graph-partitioning algorithm is used to cluster the hypergraph again. Results on real data sets are given to show that the number of clusters can be adaptively determined and clusterings combination can improve the clustering performance.