Abstract
Artificial neural network can be categorized according to the type of learning, that is, supervised learning versus unsupervised learning. Unsupervised learning can find the major features of the origin data without indication. Adaptive resonance theory can classify large various data into groups of patterns. Through analysing the limit of adaptive resonance theory, a dynamic clustering algorithm is provided. The algorithm not only can prevent from discarding irregular data or giving rise to dead neurons but also can cluster unlabelled data when the number of clustering is unknown. In the experiments, the same data are used to train the adaptive resonance theory network and the dynamic clustering algorithm network. The results prove that dynamic clustering algorithm can cluster unlabelled data correctly.
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Tian, D., Liu, Y., Shi, J. (2006). DYNAMIC CLUSTERING ALGORITHM BASED ON ADAPTIVE RESONANCE THEORY. In: LIU, G., TAN, V., HAN, X. (eds) Computational Methods. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-3953-9_35
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DOI: https://doi.org/10.1007/978-1-4020-3953-9_35
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3952-2
Online ISBN: 978-1-4020-3953-9
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