Cluster analysis of acrylates to guide sampling for toxicity testing
Journal of Chemical Information & Computer Sciences
The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Future Generation Computer Systems
Swarm intelligence
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We propose an adaptive ant colony data clustering algorithm for a dynamic database. The algorithm uses a digraph where the vertices represent the data to be clustered. The weight of the edge represents the acceptance rate between the two data connected by the edge. The pheromone on the edges is adaptively updated by the ants passing through it. Some edges with less pheromone are progressively removed under a list of thresholds in the process. Strong connected components of the final digraph are extracted as clusters. Experimental results on several real datasets and benchmarks indicate that the algorithm can find clusters more quickly and with better quality than K-means and LF. In addition, when the database is changed, the algorithm can dynamically modify the clusters accordingly to maintain its accuracy.