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IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 3, MAY 2002 645 Constructive Feedforward ART Clustering
 

Summary: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 3, MAY 2002 645
Constructive Feedforward ART Clustering
Networks--Part I
Andrea Baraldi and Ethem Alpaydin
Abstract--Part I of this paper proposes a definition of the
adaptive resonance theory (ART) class of constructive unsuper-
vised 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 opti-
mized 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.

  

Source: Alpaydın, Ethem - Department of Computer Engineering, Bogaziçi University

 

Collections: Computer Technologies and Information Sciences