Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
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
A new swarm mechanism based on social spiders colonies: from web weaving to region detection
Web Intelligence and Agent Systems
AntClust: ant clustering and web usage mining
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A survey: hybrid evolutionary algorithms for cluster analysis
Artificial Intelligence Review
A single pass algorithm for clustering evolving data streams based on swarm intelligence
Data Mining and Knowledge Discovery
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Swarm behaviors contribute to the resolution of very large number of difficult tasks thanks to simplified models and elementary rules [1]. This work claims a new swarm based behavior used for unsupervised classification. The proposed behavior starts from the ants collective sorting behavior as initially proposed by Lumer and Faieta [2] and overwrites it with additional behaviors inspired from birds and spiders. Our algorithm is then based on the existing work of [3], [4] and [2]. The proposed approach, called SwarmClass, outperforms previous ant-based clustering methods and resolve all its drawbacks by the introduction of simple swarm techniques and without the need of complex parameters configuration and prior information on classes' partition and distribution. Our proposed algorithm uses ants' segregation behavior to group similar objects together; birds' moving behavior to control next relative positions for a moving ant; and spiders' homing behavior to manage ants' movements when conflicting situations occur.