An enhancing dynamic self-organizing map for data clustering

Yu, X, Alahakoon, D and Fei, S 2013, 'An enhancing dynamic self-organizing map for data clustering', in Youxian Sun, Qing-Guo Wang, Cheng Xiang, Yucai Zhu (ed.) Proceedings of 2013 10th IEEE International Conference on Control and Automation (ICCA), Hangzhou, China, 12-14 June 2013, pp. 1324-1329.


Document type: Conference Paper
Collection: Conference Papers

Title An enhancing dynamic self-organizing map for data clustering
Author(s) Yu, X
Alahakoon, D
Fei, S
Year 2013
Conference name ICCA 2013
Conference location Hangzhou, China
Conference dates 12-14 June 2013
Proceedings title Proceedings of 2013 10th IEEE International Conference on Control and Automation (ICCA)
Editor(s) Youxian Sun, Qing-Guo Wang, Cheng Xiang, Yucai Zhu
Publisher IEEE
Place of publication United States
Start page 1324
End page 1329
Total pages 6
Abstract This paper presents a novel growing self-organizing map which features incremental learning, dynamic network structure and good visualization ability. It allows for on-line and continuous learning on both static and evolving data distributions. The experiments are carried out on some benchmark data sets for vector quantisation and clustering. Compared with the GSOM method, our results show that this new model can achieve better or comparable performance in real-world data sets.
Subjects Control Systems, Robotics and Automation
Keyword(s) Benchmark data
Continuous learning
Data clustering
Dynamic network
Evolving datum
Growing Self Organizing Map
Incremental learning
Vector quantisation
DOI - identifier 10.1109/ICCA.2013.6565029
Copyright notice © 2013 IEEE
ISBN 9781467347082
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