Learning using an artificial immune system
Journal of Network and Computer Applications - Special issue on intelligent systems: design and applications. Part 2
Cluster validity methods: part I
ACM SIGMOD Record
Clustering validity checking methods: part II
ACM SIGMOD Record
On Clustering Validation Techniques
Journal of Intelligent Information Systems
AINE: An Immunological Approach to Data Mining
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The Coevolution of Antibodies for Concept Learning
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
Application areas of AIS: The past, the present and the future
Applied Soft Computing
A stochastic nature inspired metaheuristic for clustering analysis
International Journal of Business Intelligence and Data Mining
Genetic Algorithm Based Clustering: A Survey
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
An ACO-based clustering algorithm
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Learning with case-injected genetic algorithms
IEEE Transactions on Evolutionary Computation
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Artificial immune systems inspired by humoral-mediated immunity use hyper mutation to simulate the way that natural immune systems refine their B cells and antibodies in response to pathogens in a process called affinity maturation. Such hyper mutation is typically performed on individual computational antibodies and B cells, and has been shown to be successful in a variety of machine learning tasks, including supervised and unsupervised learning. This paper proposes a population-based approach to affinity maturation in the problem domain of clustering. Previous work in humoral-mediated immune systems (HAIS), while using concepts of immunoglobulins, antibodies and B cells, has not investigated the use of population-based evolutionary approaches to evolving better antibodies with successively greater affinities to pathogens. The population-based approach described here is a two step algorithm, where the number of clusters is obtained in the first step using HAIS and then in step two a population-based approach is used to further enhance the cluster quality. Convergence in the fitness of populations is achieved through transferring memory cells from one generation to another. The experiments are performed on benchmarked real world datasets and demonstrate the feasibility of the proposed approach. Additional results also show the effectiveness of the crossover operator at the population level. The outcome is an artificial immune system approach to clustering that uses both mutation within antibodies and crossover between members of B cells to achieve effective clustering.