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Adaptive Higher Order Neural Network Models for Data Mining


Xu, S, Adaptive Higher Order Neural Network Models for Data Mining, Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications, Information Science Reference, Ming Zhang (ed), Hershey, United States, pp. 86-98. ISBN 978-1-61520-711-4 (2010) [Research Book Chapter]

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DOI: doi:10.4018/978-1-61520-711-4.ch004


Data mining, the extraction of hidden patterns and valuable information from large databases, is a powerful technology with great potential to help companies survive competition. Data mining tools search databases for hidden patterns, finding predictive information that business experts may overlook because it lies outside their expectations. This chapter addresses using ANNs for data mining because ANNs are a natural technology which may hold superior predictive capability, compared with other data mining approaches. The chapter proposes Adaptive HONN models which hold potential in effectively dealing with discontinuous data, and business data with high order nonlinearity. The proposed adaptive models demonstrate advantages in handling several benchmark data mining problems.

Item Details

Item Type:Research Book Chapter
Keywords:Data mining, neural network, higher order neural network, adaptive higher order neural network
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Xu, S (Dr Shuxiang Xu)
ID Code:65234
Year Published:2010
Deposited By:Information and Communication Technology
Deposited On:2010-10-19
Last Modified:2015-02-13
Downloads:3 View Download Statistics

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