Dynamic self-organizing maps with controlled growth for knowledge discovery

By Damminda Alahakoon, Saman Kumara Halgamuge, Bala Srinivasan. IEEE Trans. Neural Networks Learn. Syst., 2000

564 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f3093f3564ca6818fd4cbdb138c20c419d23ced4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Upuli Gunasinghe
2. Damminda Alahakoon
3. Su Nguyen
4. Wee Keong Ng
5. Yew Kwong Woon
6. Hai-Long Nguyen

In Computer Vision and Pattern Recognition

1. Upuli Gunasinghe
2. Damminda Alahakoon
3. Wee Keong Ng
4. Yew Kwong Woon
5. Hai-Long Nguyen

Citations in the corpus

• Sequence learning using the adaptive suffix trie algorithm (2012, methodology): “The Self Organizing Map(SOM)[14] and the Growing Self Organizing Map(GSOM)[15] are unsupervised clustering algorithms which make use of Hebbian Learning.”
• Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum (2003, unknown): “LOGSOM: Web usage mining by a self-organizing map.”
• A survey on data stream clustering and classification (2015, background): “–GCPSOM [87]: There are two important extensions of SOM: Growing self-organizing map (GSOM)[10] and cellular probabilistic self-organizing map (CPSOM) [28].”
• Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management (2019, methodology): “The functionality of the learning layer is based on Growing Self Organizing Maps (GSOM) [24] that generates a dynamic feature map based on the growing self-organizing process.”