A knowledge-driven ART clustering algorithm
Zhaoyang Sun, Lee Onn Mak, Kezhi Mao, Wenyin H. S. Tang, Ying Liu, Kuitong Xian, Zhimin Wang, Yuan Sui · 2014
In applications such as target detection, domain knowledge of sensed data is often available. In this paper, we incorporate the available domain knowledge into clustering process and develop a knowledge-driven Mahalanobis distance-based ART (adaptive resonance theory) clustering algorithm. The strength of the knowledge-driven algorithm is that it can automatically determine the number of clusters with improved clustering results. The validity of the new algorithm has been verified on four artificial datasets. In addition, the algorithm has been adopted in our cognition-inspired target detection and classification system, where known target library and dispersion of feature or attributes are available.