Evolving Self-Organizing Maps for On-line Learning, Data Analysis and Modelling

In real world information systems, data analysis and processing are usually needed to be done in an on-line, self-adaptive way. In this respect, neural algorithms of incremental learning and constructive network models are of increased interest. In this paper we present a new algorithm of evolving self-organizing map (ESOM), which features fast one-pass learning, dynamic network structure, and good visualisation ability. Simulations have been carried out on some benchmark data sets for classification and prediction tasks, as well as on some macroeconomic data for data analysis. Compared with other methods, ESOM achieved better classification with much shorter learning time. Its performance for time series modelling is also comparable, requiring more hidden units but with only one-pass learning. Our results demonstrate that ESOM is an effective computational model for on-line learning, data analysis and modelling.

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