An artificial immune system for data analysis.

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A survey on bio-inspired networking (2010, background): “Pioneering work by Timmis and co-workers needs to be mentioned who conceptually analyzed the AIS and applied it to several problem domains [69, 11, 36].”
Fundamentals of natural computing: an overview (2007, neutral): “, [74,112,201,268]).”
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm (2004, methodology): “Other work in [30] also utilized the immune network theory metaphor for unsupervised learning, and then augmented the work with the development of a resource limited artificial immune network [29], which reported good benchmark results for cluster extraction and exploration with artificial immune ne”
Ant-based and swarm-based clustering (2007, methodology): “To the best of the authors’ knowledge the idea to use AIS for clustering goes back to (Timmis et al. 1999), with more complete descriptions given in (Timmis et al. 2000; Timmis and Neal 2001).”
Advances in artificial immune systems (2006, positive): “Monokines that the combination of normalizing the stimulation levels of ARBs in the network and the resource allocation mechanism leads to the biasing of AINE toward the strongest pattern in the data set to emerge [33].”
A resource limited artificial immune system for data analysis (2001, methodology): “Work in [14] builds on the foundations laid in [8] and [12].”