Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Learning using an artificial immune system
Journal of Network and Computer Applications - Special issue on intelligent systems: design and applications. Part 2
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
A comparative analysis of artificial immune network models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Immune network based ensembles
Neurocomputing
Application areas of AIS: The past, the present and the future
Applied Soft Computing
A hybrid artificial immune system and Self Organising Map for network intrusion detection
Information Sciences: an International Journal
A scalable artificial immune system model for dynamic unsupervised learning
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
Revisiting the Foundations of Artificial Immune Systems for Data Mining
IEEE Transactions on Evolutionary Computation
A Quick Assessment of Topology Preservation for SOM Structures
IEEE Transactions on Neural Networks
Clonal selection algorithm for learning concept hierarchy from Malay text
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
A hybrid approach for learning concept hierarchy from Malay text using artificial immune network
Natural Computing: an international journal
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The architecture and learning procedure of a novel artificial immune network, referred to as tree structured artificial immune network (TSAIN), are described in this paper. One major difference between this model and current models is that the topological structure can be strictly guaranteed as a tree, which allows to analyze the presented data (antigens) hierarchically. The other is that a novel antibody reaction mechanism inspired from the self-organizing map (SOM) is adopted in order to maintain consistency between the shape space metric and the topological metric, which is an important objective in high-dimensional data analysis. Moreover, several novel immune operators are also employed to improve the quality of the antibody population as well as to control its size. It is qualitatively demonstrated as well as quantitatively verified on a 3-D synthetic dataset and Iris dataset that TSAIN exhibits promising data visualization capability and low vector quantization error. We also use three well-known topology preservation measures to validate the topology preservation capability of our proposed model.