Adaptive Higher Order Neural Network Models for Data Mining

Adaptive Higher Order Neural Network Models for Data Mining

Shuxiang Xu (University of Tasmania, Australia)
DOI: 10.4018/978-1-61520-711-4.ch004
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Abstract

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.
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Background

Data mining is usually supported by the following technologies: massive data collection, powerful multiprocessor computers, and data mining algorithms.

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