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Title: A new effective learning rule of Fuzzy ART
Authors: Hoa, Nong Thi
Bui, The Duy
Keywords: Clustering;Fuzzy adaptive resonance theory;Learning rule;Quality of clustering;Neural networks;Learning systems
Issue Date: 2012
Publisher: H. : ĐHQGHN
Abstract: Unsupervised neural networks are known for their ability to cluster inputs into categories based on the similarity among inputs. Fuzzy Adaptive Resonance Theory (Fuzzy ART) is a kind of unsupervised neural networks that learns training data until satisfying a given need. In the learning process, weights of categories are changed to adapt to noisy inputs. In other words, learning process decides the quality of clustering. Thus, updating weights of categories is an important step of learning process. We propose a new effective learning rule for Fuzzy ART to improve clustering. Our learning rule modifies weights of categories based on the ratio of the input to the weight of chosen category and a learning rate. The learning rate presents the speed of increasing/decreasing the weight of chosen category. It is changed by the following rule: the number of inputs is larger, value is smaller. We have conducted experiments on ten typical datasets to prove the effectiveness of our novel model. Result from experiments shows that our novel model clusters better than existing models, including Original Fuzzy ART, Complement Fuzzy ART, K-mean algorithm, Euclidean ART.
Description: Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012, Article number 6395033, Pages 224-231
Appears in Collections:Bài báo của ĐHQGHN trong Scopus

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