Title :
Ellipsoid ART and ARTMAP for incremental clustering and classification
Author :
Anagnostopoulos, Georgios C. ; Georgiopoulos, Michael
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci., Central Florida Univ., Orlando, FL, USA
Abstract :
We introduce ellipsoid-ART (EA) and ellipsoid-ARTMAP (EAM) as a generalization of hypersphere ART (HA) and hypersphere-ARTMAP (HAM) respectively. As was the case with HA/HAM, these novel architectures are based on ideas rooted in fuzzy-ART (FA) and fuzzy-ARTMAP (FAM). While FA/FAM aggregate input data using hyper-rectangles, EA/EAM utilize hyper-ellipsoids for the same purpose. Due to their learning rules, EA and EAM share virtually all properties and characteristics of their FA/FAM counterparts. Preliminary experimentation implies that EA and EAM are to be viewed as good alternatives to FA and FAM for data clustering and classification tasks respectively
Keywords :
ART neural nets; fuzzy neural nets; generalisation (artificial intelligence); learning (artificial intelligence); pattern classification; pattern clustering; ART neural nets; data clustering; ellipsoid-ART; ellipsoid-ARTMAP; fuzzy neural nets; generalization; hyper-ellipsoids; hyper-rectangles; hypersphere ART; hypersphere-ARTMAP; learning rules,; Aggregates; Buildings; Clustering algorithms; Computer science; Ellipsoids; Feedforward neural networks; Feedforward systems; Neural networks; Resonance; Subspace constraints;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.939535
