Pseudo-Hierarchical Ant-Based Clustering

The behavior and self-organization of ant colonies has been widely studied to address distributed clustering. However, most models that directly mimic ants produce too many clusters and converge too slowly. A wide range of research has attempted to address this through various means, but a number of sources of inefficiency remain, including: i) ants must physically move from one cluster to another through intermediate locations, ii) patterns in movement among clusters is not considered, and iii) while some approaches have included bulk item movement, they do not provide efficient movement while still maintaining the self-organizing nature of ant-based clustering. To address these issues, this paper presents a new algorithm for ant-based clustering. Here ants maintain a movement zone around each cluster, keeping ants close to data items. These movement zones are used to elect representatives that are responsible for all long distance movement. Representatives can, probabilistically, pass an object it has to any other representative. Since each cluster has approximately one representative at any given time, the search space for placing items over a long distance is reduce to the number of clusters. This provides an infrastructure that allows bulk movement and efficient long distance merging.