An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering
Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Olena, O. Boiko
TL;DR
This paper introduces an ensemble method using adaptive neuro-fuzzy Kohonen networks for online data stream clustering, leveraging parallel processing and selecting the best map for improved clustering performance.
Contribution
It presents a novel ensemble approach combining adaptive neuro-fuzzy Kohonen maps for real-time data stream clustering, enhancing adaptability and accuracy.
Findings
Effective clustering of data streams demonstrated
Ensemble approach improves robustness
Parallel processing accelerates computation
Abstract
A new approach to data stream clustering with the help of an ensemble of adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing mode. A final result is chosen by the best neuro-fuzzy self-organizing Kohonen map.
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Taxonomy
TopicsAdvanced Clustering Algorithms Research · Data Stream Mining Techniques · Metaheuristic Optimization Algorithms Research