Comparative Analysis of Fuzzy ART and ART-2A Network Clustering Performance  

Oleh:

Kuhlen, T. ; Frank, T. ; Kraiss, K.-F.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 9 no. 3 (1998), page 544-559.
Topik: CLUSTERING; comparative analysis; fuzzy ART; ART - 2A; network; clustering; performance

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.3 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Adaptive resonance theory (ART) describes a family of self - organizing neural networks, capable of clustering arbitrary sequences of input patterns into stable recognition codes. Many different types of ART networks have been developed to improve clustering capabilities. We compare clustering performance of different types of ART networks : fuzzy ART, ART 2A with and without complement encoded input patterns, and a Euclidean ART 2A - variation. All types are tested with two - and high - dimensional input patterns in order to illustrate general capabilities and characteristics in different system environments. Based on our simulation results, fuzzy ART seems to be less appropriate whenever input signals are corrupted by addititional noise, while ART 2A - type networks keep stable in all inspected environments. Together with other examined features, ART architectures suited for particular applications can be selected.

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