Adaptive Resonance Theory-Based Topological Clustering with Node Deletion Mechanism for Evolving Stream Data
Author:
Affiliation:
1. Graduate School of Informatics, Osaka Metropolitan University,Sakai,Japan
Funder
Japan Society for the Promotion of Science (JSPS)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10327787/10327919/10328009.pdf?arnumber=10328009
Reference12 articles.
1. Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning
2. Fast Topological Adaptive Resonance Theory Based on Correntropy Induced Metric
3. Statistical comparisons of classifiers over multiple data sets;demšar;Journal of Machine Learning Research,2006
4. Correntropy: Properties and applications in non-Gaussian signal processing;weifeng;IEEE Trans on Signal Processing,2007
5. An evolving approach to data streams clustering based on typicality and eccentricity data analytics
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