Constructive Feedforward ART Clustering Networks—Part II
نویسنده
چکیده
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.
منابع مشابه
Constructive Feedforward ART Clustering Networks—Part I
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...
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تاریخ انتشار 2001