ACM Computing Surveys (CSUR)
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
Application areas of AIS: The past, the present and the future
Applied Soft Computing
Clustering Without Prior Knowledge Based on Gene Expression Programming
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Automatic kernel clustering with a Multi-Elitist Particle Swarm Optimization Algorithm
Pattern Recognition Letters
Automatic image pixel clustering with an improved differential evolution
Applied Soft Computing
Automatic Clustering Using a Synergy of Genetic Algorithm and Multi-objective Differential Evolution
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A bacterial evolutionary algorithm for automatic data clustering
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Automatic clustering with multi-objective differential evolution algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm
Information Sciences: an International Journal
Baldwinian learning in clonal selection algorithm for optimization
Information Sciences: an International Journal
Data Clustering Using Multi-objective Differential Evolution Algorithms
Fundamenta Informaticae
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
A Jumping Gene Paradigm for Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
Clustering construction on a multimodal probability model
Information Sciences: an International Journal
Fast global k-means clustering based on local geometrical information
Information Sciences: an International Journal
Analysing microarray expression data through effective clustering
Information Sciences: an International Journal
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Inspired by the principle of gene transposon proposed by Barbara McClintock, a new immune computing algorithm for automatic clustering named as Gene Transposon based Clone Selection Algorithm (GTCSA) is proposed in this paper. The proposed algorithm does not require a prior knowledge of the number of clusters; an improved variant of the clonal selection algorithm is used to determine the satisfied number of clusters and the appropriate partitioning of the data set as well. In addition, a novel operation called antibody gene transposon is introduced to the framework of clonal selection algorithm which can realize to find the satisfied number of cluster automatically. The proposed method has been extensively compared with iterated local search approach (ILS) and three well-known automatic clustering algorithms, including automatic clustering using an improved differential evolution algorithm (ACDE); variable-string-length genetic algorithm based clustering techniques (VGA) and the dynamic clustering approach based on particle swarm optimization (DCPSO). 23 datasets with widely varying characteristics are used to demonstrate the superiority of the GTCSA. In addition, GTCSA is applied to a real world application, namely natural image segmentation, with a good performance obtained.