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
Emergent colonization and graph partitioning
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
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
Parallel algorithms for hierarchical clustering
Parallel Computing
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
The ant colony optimization meta-heuristic
New ideas in optimization
Future Generation Computer Systems
Self-Organizing Maps
Improved Ant-Based Clustering and Sorting
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Homogeneous Ants for Web Document Similarity Modeling and Categorization
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Proceedings of the 2005 ACM symposium on Applied computing
Ant-Based Clustering and Topographic Mapping
Artificial Life
Color reduction based on ant colony
Pattern Recognition Letters
Aggregation pheromone density based data clustering
Information Sciences: an International Journal
Enhanced swarm-like agents for dynamically adaptive data clustering
CEA'08 Proceedings of the 2nd WSEAS International Conference on Computer Engineering and Applications
A Set-Based Particle Swarm Optimization Method
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization
Expert Systems with Applications: An International Journal
Use of aggregation pheromone density for image segmentation
Pattern Recognition Letters
Heuristic Search for Cluster Centroids: An Ant-Based Approach for FCM Initialization
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
An improved probabilistic ant based clustering for distributed databases
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
Measuring stigmergy: the case of foraging ants
ESOA'06 Proceedings of the 4th international conference on Engineering self-organising systems
Ant clustering algorithm with K-harmonic means clustering
Expert Systems with Applications: An International Journal
EURASIP Journal on Wireless Communications and Networking - Special issue on radar and sonar sensor networks
Topological hierarchical tree using artificial ants
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering
Expert Systems with Applications: An International Journal
A new hybrid method based on partitioning-based DBSCAN and ant clustering
Expert Systems with Applications: An International Journal
A survey: hybrid evolutionary algorithms for cluster analysis
Artificial Intelligence Review
Dynamic decentralized packet clustering in networks
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Aggregation pheromone density based image segmentation
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Contextual web searches in Facebook using learning materials and discussion messages
Computers in Human Behavior
Self-Organizing Tree Using Artificial Ants
Journal of Information Technology Research
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Ant-based clustering and sorting is a nature-inspired heuristic for general clustering tasks. It has been applied variously, from problems arising in commerce, to circuit design, to text-mining, all with some promise. However, although early results were broadly encouraging, there has been very limited analytical evaluation of the algorithm. Toward this end, we first propose a scheme that enables unbiased interpretation of the clustering solutions obtained, and then use this to conduct a full evaluation of the algorithm. Our analysis uses three sets each of real and artificial data, and four distinct analytical measures. These results are compared with those obtained using established clustering techniques and we find evidence that ant-based clustering is a robust and viable alternative.