Abstract :
Part I of this paper proposes a definition of the adaptive resonance theory (ART) class of constructive unsupervised on-line learning clustering networks. Class ART generalizes several well-known clustering models, e.g., ART 1, improved ART 1, adaptive Hamming net (AHN), and Fuzzy ART, which are optimized in terms of memory storage and/or computation time. Next, the symmetric Fuzzy ART (S-Fuzzy ART) network is presented as a possible improvement over Fuzzy ART. As a generalization of S-Fuzzy ART, the simplified adaptive resonance theory (SART) group of ART algorithms is defined. Gaussian ART (GART), which is found in the literature, is presented as one more instance of class SART. In Part II of this work, a novel SART network, called fully self-organizing SART (FOSART), is proposed and compared with Fuzzy ART, S-Fuzzy ART, GART and other well-known clustering algorithms. Results of our comparison may easily extend to the ARTMAP supervised learning framework
Keywords :
ART neural nets; feedforward neural nets; fuzzy neural nets; pattern clustering; self-organising feature maps; unsupervised learning; ARTMAP; Fuzzy ART neural network; GART; Gaussian ART; S-Fuzzy ART; adaptive Hamming net; computation time; constructive feedforward ART clustering networks; fully self-organizing SART; memory storage; simplified adaptive resonance theory network; supervised learning; symmetric Fuzzy ART; unsupervised online learning clustering networks; Artificial neural networks; Clustering algorithms; Fuzzy neural networks; Fuzzy systems; Partitioning algorithms; Random sequences; Resonance; Subspace constraints; Supervised learning; Unsupervised learning;
