Growing recurrent self organizing map

— The growing Recurrent Self-Organizing Map (GRSOM) is embedded into a standard Self-Organizing Map (SOM) hierarchy. To do so, the KDD benchmark dataset from the International Knowledge Discovery and Data Mining Tools Competition is employed. This dataset consists of 500,000 training patterns and 41 features for each pattern. Unlike most of the previous methods, only 6 of the basic features are employed. The resulting model has a capability of detection (false positive) rate of 89.6% (5.66%), where this is as good as the data-mining approaches that uses all 41 features and twice as faster than a similar hierarchical SOM architecture.

• Type: Conference
• Year: 2007
• Where: SMC
• Authors: Ozge Yeloglu, A. Nur Zincir-Heywood, Malcolm I. Heywood
• Keywords: Control Systems, Recurrent Self-Organizing Map, Self-Organizing Map, SMC 2007, Standard Self-organizing Map