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Improving pattern discovery and visualisation with self-adaptive neural networks through data transformations

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Abstract

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.

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Authors and Affiliations

  1. School of Computing and Mathematics, Computer Science Research Institute, University of Ulster, Shore Road, Newtownabbey, Co. Antrim, BT37 0QB, UK

    Huiru Zheng & Haiying Wang

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  1. Huiru Zheng
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  2. Haiying Wang
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Correspondence to Huiru Zheng.

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Zheng, H., Wang, H. Improving pattern discovery and visualisation with self-adaptive neural networks through data transformations. Int. J. Mach. Learn. & Cyber. 3, 173–182 (2012). https://doi.org/10.1007/s13042-011-0050-z

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  • DOI: https://doi.org/10.1007/s13042-011-0050-z

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