Abstract :
For pt.I see ibid., p.645-61 (2002). Part I of this paper defines the class of constructive unsupervised on-line learning simplified adaptive resonance theory (SART) clustering networks. Proposed instances of class SART are the symmetric fuzzy ART (S-Fuzzy ART) and the Gaussian ART (GART) network. In Part II of our work, a third network belonging to class SART, termed fully self-organizing SART (FOSART), is presented and discussed. FOSART is a constructive, soft-to-hard competitive, topology-preserving, minimum-distance-to-means clustering algorithm capable of: 1) generating processing units and lateral connections on an example-driven basis and 2) removing processing units and lateral connections on a minibatch basis. FOSART is compared with Fuzzy ART, S-Fuzzy ART, GART and other well-known clustering techniques (e.g., neural gas and self-organizing map) in several unsupervised learning tasks, such as vector quantization, perceptual grouping and 3-D surface reconstruction. These experiments prove that when compared with other unsupervised learning networks, FOSART provides an interesting balance between easy user interaction, performance accuracy, efficiency, robustness, and flexibility
Keywords :
ART neural nets; feedforward neural nets; fuzzy neural nets; pattern clustering; self-organising feature maps; unsupervised learning; 3D surface reconstruction; FOSART; Gaussian ART network; adaptive resonance theory clustering networks; constructive feedforward ART clustering networks; experiments; fully self-organizing SART; minimum-distance-to-means clustering algorithm; perceptual grouping; symmetric fuzzy ART; topology-preserving; unsupervised online learning; user interaction; vector quantization; Clustering algorithms; Cost function; Lattices; Network topology; Parameter estimation; Prototypes; Resonance; Subspace constraints; Unsupervised learning; Vector quantization;
