Clustering ensemble using swarm intelligence
Yan Zhu Yang, Mohamed S. Kamel · 2004
This paper presents a clustering ensemble using three colonies of ants, each colony having different ant speed model: constant, random, and randomly decreasing. The algorithm is a two-phase process. Initially clusterings are visually formed on the plane by ants walking, picking up or dropping down projected data objects with different probability, and then a hypergraph model is used to combine clusterings. Results on synthetic and real data sets are given to show that the number of clusters can be adaptively determined and clustering ensembles can improve the clustering performance.