International Journal of Machine Learning and Cybernetics

, Volume 3, Issue 3, pp 173–182

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

Original Article

DOI: 10.1007/s13042-011-0050-z

Cite this article as:
Zheng, H. & Wang, H. Int. J. Mach. Learn. & Cyber. (2012) 3: 173. doi:10.1007/s13042-011-0050-z

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.

Keywords

Self-adaptive neural networksPattern discovery and visualisationSimilarity measureChi-squares statistics

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.School of Computing and Mathematics, Computer Science Research InstituteUniversity of UlsterCo. AntrimUK