A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Remote Sensing and Image Interpretation
Remote Sensing and Image Interpretation
Clustering Without Prior Knowledge Based on Gene Expression Programming
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Inspired by the principle of gene transposon proposed by Barbara McClintock, a new immune computing algorithm for clustering multi-class data sets named as Gene Transposition based Clone Selection Algorithm (GTCSA) is proposed in this paper, The proposed algorithm does not require a prior knowledge of the numbers of clustering; an improved variant of the clonal selection algorithm has been used to determine the number of clusters as well as to refine the cluster center. a novel operator called antibody transposon is introduced to the framework of clonal selection algorithm which can realize to find the optimal number of cluster automatically. The proposed method has been extensively compared with Variable-string-length Genetic Algorithm(VGA)based clustering techniques over a test suit of several real life data sets and synthetic data sets. The results of experiments indicate the superiority of the GTCSA over VGA on stability and convergence rate, when clustering multi-class data sets.