Improving pattern discovery and visualisation with self-adaptive neural networks through data transformations
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The ability to reveal the relevant patterns in an intuitively attractive way through incremental learning makes self-adaptive neural networks (SANNs) a power tool to support pattern discovery and visualisation. Based on the combination of the information related to both the shape and magnitude of the data, this paper introduces a SANN, which implements new similarity matching criteria and error accumulation strategies for network growth. It was tested on two datasets including a real biological gene expression dataset. The results obtained have demonstrated several significant features exhibited by the proposed SANN model for improving pattern discovery and visualisation.