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.
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