A multi-agent-based algorithm for data clustering

Clustering algorithms aim to detect groups based on similarity, from a given set of objects. Many clustering techniques have been proposed, most requiring the user to set critical parameters, such as the number of groups. This work presents the implementation and evaluation of a clustering algorithm based on a multi-agent system, which automatically detects the number of groups and the group labels for a given dataset. Groups formed during the clustering process emerge as patterns from the interaction among agents. The proposed algorithm is experimentally validated over benchmark datasets from the literature. The quality of clustering results is computed using seven internal indexes and one external index. Under this methodology, the proposed algorithm is compared to K-means and DBSCAN (density-based spatial clustering of applications with noise).