DYNAMIC CLUSTERING ALGORITHM BASED ON ADAPTIVE RESONANCE THEORY

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.

[1]  Hirotaka Inoue,et al.  Efficiency of self-generating neural networks applied to pattern recognition , 2003 .

[2]  Alessio Micheli,et al.  A general framework for unsupervised processing of structured data , 2004, Neurocomputing.

[3]  Giuseppe Acciani,et al.  A feature extraction unsupervised neural network for an environmental data set , 2003, Neural Networks.

[4]  Stephen R. Marsland,et al.  A self-organising network that grows when required , 2002, Neural Networks.

[5]  Michael Gribskov,et al.  Rival penalized competitive learning (RPCL): a topology-determining algorithm for analyzing gene expression data , 2003, Comput. Biol. Chem..

[6]  Yunhui Liu,et al.  A Game-Theoretic Adaptive Categorization Mechanism for ART-Type Networks , 2001, ICANN.

[7]  Habtom W. Ressom,et al.  Adaptive double self-organizing maps for clustering gene expression profiles , 2003, Neural Networks.

[8]  Ryotaro Kamimura.  Competitive Learning by Information Maximization: Eliminating Dead Neurons in Competitive Learning , 2003, ICANN.

[9]  José Antonio Gómez-Ruiz,et al.  A Competitive Neural Network Based on Dipoles , 2003, IWANN.

[10]  Andrew Luk.  Analyses on the Generalised Lotto-Type Competitive Learning , 2000, IDEAL.

[11]  Yunhui Liu,et al.  Adaptive categorization of ART networks in robot behavior learning using game-theoretic formulation , 2003, Neural Networks.

[12]  Miin-Shen Yang,et al.  A fuzzy-soft learning vector quantization , 2003, Neurocomputing.

[13]  Lei Xu,et al.  Local PCA for Strip Line Detection and Thinning , 2003, EMMCVPR.