Improvements In Flock-Based Collaborative Clustering Algorithms

Inspiration from nature has driven many creative solutions to challenging real life problems. Many optimization methods, in particular clustering algorithms, have been inspired by such natural phenomena as neural systems and networks, natural evolution, the immune system, and lately swarms and colonies. In this paper, we make a brief survey of swarm intelligence clustering algorithms and focus on the flocks of agents-based clustering and data visualization algorithm, (FClust). A few limitations of FClust are then discussed with proposed improvements. We thus propose the FClust-annealing algorithm that decreases the number of iterations needed to converge and improves the quality of resulting clusters. We also propose a (K-means+FClust) hybrid algorithm which decreases the complexity of FClust from quadratic to linear, with further improvements in the cluster quality. Experiments on both artificial and real data illustrate the workings of FClust and the advantages of our proposed variants.